-
Roughly, a motivator is derivative if it is explicitly derived
from another motivator by means-ends analysis and this origin is recorded
and plays a role in subsequent processing. A desire to drink when thirsty
would be nonderivative, whereas a desire for money to buy the drink would
be derivative.
The affective reasoner is concerned only with manipulations of the
nonderivative motivators which lead directly to the emotion inference
mechanism.
Among the emotion-reasoning systems, the Affective Reasoner and
ACRES may be characterized as approaches that deal with the representational
issues of emotions proper, and of the processes that generate them. By
contrast OpEd, SIES and AMAL may be seen as more general approaches that
have an emphasis on reasoning within the object domain about
situations that give rise to emotions. BORIS and THUNDER sit somewhere in
the middle. In other words, the Affective Reasoner and ACRES focus on the
nonderivative motivators which lead to emotions whereas the other systems
have varying degrees to which they also concentrate on the nonderivative
motivators. Note also, that while the Affective Reasoner does little
reasoning within the object domain itself, it does have an associated system
for indexing stories in an object domain according to the underlying
initiators of emotions (see [10]).
In this sense it is similar to SIES.
Of these programs, only ACRES gives any treatment at all to the
biological systems involved in the production of emotions, and then only
cursorily. The remainder (the Affective Reasoner included), only deal with
the cognitive aspects of emotions.
The earliest well-known computer model of emotion reasoning was
Kenneth Colby's PARRY [5], a program that mimicked the behavior
of a schizophrenic paranoid personality. A concise account of PARRY is
provided by Ortony [17] who writes,
- Colby's program, PARRY, accepted English-like input and responded as
a patient in a psychiatric interview by attempting to construe inputs as
directly or indirectly indicative of malevolence. If malevolence was
detected, one or more of three affective responses fear, anger, or
mistrust was triggered, depending upon the nature of the construed
malevolence. Physical malevolence induced fear, psychological malevolence
anger, and the induction of either also induced mistrust. The linguistic
output was designed to offset the construed malevolence either through
counterattack or withdrawal. In all cases, the nature of the construals and
of the responses was determined by the constantly updated values of a number
of key variables, including those for fear, anger, and mistrust.
The Affective Reasoner, like PARRY, also models agents who ``have''
emotions. In addition, both systems maintain internal dynamic states, which
affect the way in which the modeled agent(s) construe the perceived world
and manifest the emotions resulting from those construals. Starting with a
psychological model, PARRY attempted to model a true paranoid personality.
By contrast, the Affective Reasoner uses an ad hoc theory of
rudimentary personality representation designed to be broadly applicable
rather than focused on a specific psychological syndrome.
More recently, another system, BORIS [8], analyzed text
for affective responses to goal states and interpersonal relationships. In
this system, Dyer used ACE structures (Affect as a Consequence of Empathy)
to account for a number of inference mechanisms that included
affective content. In particular he developed the idea that understanding
affect can allow one to produce a causally coherent analysis of stories. In
BORIS, Dyer does not make a distinction between principles, goals and
preferences. This is important because the Affective Reasoner uses this
distinction to resolve some ambiguities. For example, a person may turn
himself in after committing a crime because it upholds a standard of
honesty, even though it clearly violates his goal of being free. Such
conflict between goals and principles are a common theme of human emotional
life and give us important leverage for reasoning in the affective
domain.
Dyer uses a six-slot frame structure to represent his ``affects''.
By contrast, the Affective Reasoner uses a hierarchy of loosely-structured
frames to represent interpretation schemas, and a simple nine-attribute
relation (called the Emotion Eliciting Condition Relation) to represent
emotion types. There are a number of reasons this latter approach is better
suited to the type of reasoning we do. In the first place, frame hierarchies
allow for the use of slot inheritance. This is useful when specifying the
interpretation schemas used for appraising emotion eliciting situations.
That is, properties may be shared by many interpretation schemas and yet can
be specified once in an ancestor, and then inherited by children frames. For
example, when a team scores a touchdown during a football game it may elicit
emotions in the fans. However, much of what defines this as an emotion
eliciting situation has to do with the nature of games in general, and at an
even higher level, with entertainment. Interpretation schemas may inherit
slots that specify the associated goals as entertainment
goals instead of health goals, and descriptive slots such as team-that-scored-the-goal, etc. The use of frames allows a great deal of
efficiency in making use of the system's stored knowledge about situations.
The frames used in this flexible representation of an agent's
interpretation schemas should not be confused with the simple relations
representing the emotions. Both BORIS and the Affective Reasoner use such
relations to represent emotions, although interestingly BORIS represents
these relations as frames. Here there is no room for inheritance: each
attribute of the tuple is specified and has an associated value. To give
inheritance to such an emotion specifier is to imply that one emotion is a
child of some other emotion, and, as such, derives value from the parent.
(Such a position is viable, but it is not one taken either by the Affective
Reasoner or BORIS.)
One result that the BORIS work shares with the Affective Reasoner
and the work by Ortony, et al. [18] is in suggesting valid, but
generally unnamed, emotion types. These arise when certain configurations of
the attributes in the Emotion Eliciting Condition Relations (to use the
Affective Reasoner's term) seem to give rise to a recognizable, but unnamed
set of emotional states in the agent. Dyer refers to the case where 
feels negative toward as a result of a goal failure caused by 
and
gives the example of a woman who felt foolish and castigated herself for
having had her purse stolen by a thief [8]. Ortony, et al.
identify the class of emotions that arise when one's fears have been
confirmed. They call these, appropriately, the fears-confirmed type
of emotions. In the current Affective Reasoner research questions are
raised about one's goals being blocked by the upholding of standards (i.e.,
``doing the right thing'' which has a bittersweet, heroic quality), and
having one's goals furthered by the violation of standards.
Related work has been done by Dyer on OpEd [9], which
understands newspaper editorial text. In the OpEd architecture, frames are
used to represent conceptual dependency structures [26]
which form belief networks about the world. Inferencing is guided by
knowledge of affect which is incorporated into the knowledge base. For
example, in trying to understand the meaning of a piece of text, the word
``disappointed'' causes demons to be spawned that resolve pronoun references
and search for blocked goals. This processing all falls into the category of
plausible reasoning within the object domain which, as previously discussed,
is beyond the scope of the Affective Reasoner's focus.
The THUNDER (THematic UNDerstading from Ethical Reasoning) program
of Reeves [21], is a story-understanding system that focuses on
evaluative judgment and ethical reasoning. Schema called Belief
Conflict Patterns are used to represent the different points of view in a
conflict situation. This work is related to that of the Affective Reasoner
in that the kind of moral reasoning needed to understand the stories that
THUNDER takes as input involves the same sorts of knowledge required to
generate the attribution and compound emotions. One of Reeves's
contributions is in the detailed working out of a set of moral patterns at
the level of stories. Reeves develops the idea that evaluators of
stories make many moral judgments about the characters in those stories
using these patterns, and that this moral framework is often necessary for
understanding text. For example, Reeves analyzes a story in which hunters
tie dynamite to a rabbit ``for fun''. The rabbit runs under their car for
cover and when the dynamite blows up it destroys their car. Reeves argues
that to understand the irony in this story the evaluator must know that
killing a rabbit with dynamite for entertainment is reprehensible. This
allows the judgment that the chance destruction of the car is an example of
morally satisfying retribution.
Clearly moral reasoning is closely related to the rise of emotions.
In both THUNDER and the Affective Reasoner, the focus is on the point of
view of individual agents within, or observing, a situation. Reeves writes:
``...using different value systems will produce different ethical
evaluations. For example, the actions of a high school student who killed
himself after failing a test would be judged ethically wrong by a Catholic,
but not by a Samurai...'' ([21], page 23). The Affective
Reasoner has no text understanding mechanism, but it could simulate
the suicide scene with various observers responding with different emotions,
dependent upon their (moral) principles.
One area that has not been focused upon in great depth is the action
generation component of emotion reasoning. Most action schemes in AI
research focus on the logically defined needs of the agent, (i.e., actions
as a function of planning for goals). Since emotions themselves are so
poorly understood and so seldom incorporated into experimental AI
systems, it is understandable that little work is being done that uses them
as motivations for action. One system that does function in this way,
however, is the ACRES program (Artificial Concern REalisation System)
developed by Frijda and Swagerman [12] in the Netherlands.
The main premise of their research is that emotions provide
a way of operating successfully in an uncertain environment (see also
[28]). They approach the design of their system from a
functional standpoint: if an agent is to be able to respond functionally
to its environment, what properties must the subsystem implementing
that functionality have? To this end, in contrast to the
Affective Reasoner's descriptive approach, the primary emphasis is
placed on behavior potentials and the search for reasons to
initiate them. To quote:
-
The major phenomena are: the existence of the feelings of pleasure
and pain, the importance of cognitive or appraisal variables, the
presence of innate, pre-programmed behaviors as well as of complex
constructed plans for achieving emotion goals, and the occurrence of
behavioral interruption, disturbance and impulse-like priority of emotional
goals [12], (page 235).
Their architecture is designed to meet these demands:
- The system properties underlying these phenomena are facilities
for relevance detection of events with regard to the multiple concerns,
availability of relevance signals that can be recognized by the action
system, and facilities for control precedence, or flexible goal priority
ordering and shift (page 235).
The Affective Reasoner also addresses some of these issues, although
the emphasis is different. The Frijda-Swagerman action architecture arises
from issues relative to processing needs, whereas ours arises from a
description of actions. Similarities can be found on a number of fronts.
Both systems incorporate the idea of the cyclic nature of emotion machinery:
a situation arises, it is evaluated, it is either ignored or a response is
initiated which is fed back to the modeled world. As with the
Affective Reasoner, ACRES performs no reasoning about derivative motives.
Instead, emotion triggers are directly wired into the architecture.
The Affective Reasoner and ACRES also both stress the importance of
concern realization as a way of filtering out situations that are
not of interest, and of interpreting those that are. Entities have multiple
concerns and limited resources. To solve this problem both programs focus on
matching as an efficient means of assessing emotion eliciting
situations. To quote Frijda and Swagerman, ``Concerns can be thought of
as embodied in internal representations against which actual conditions are
matched,'' [12] (page 237), which is exactly the conceptual
approach of the Affective Reasoner.
The two systems differ, however, in the way in which they use the
realized concerns. Of primary importance to ACRES (but only of incidental
importance to agents in the Affective Reasoner) is filtering out situations
irrelevant to resource conservation (as most situations are). In addition,
unlike the Affective Reasoner, ACRES stresses the correct interpretation of
situations with respect to urgency (as distinct from importance- see
[28]). To approximate this behavior ACRES uses a system of
interrupts.
There are a number of interesting issues that ACRES does not
address. One of these is the maintaining of expectations with respect to
future goals. The aging of expectations and the elicitation of associated
prospect-based emotions is important for systems representing emotional
behavior, as is the representation of the emotional status of other agents.
ACRES does not maintain internal models of other agents, nor does it
maintain a working memory for the storage of currently active relationships.
Without these features it is not possible to reason about the emotional
lives, and therefore motivations, of other agents with whom it must
interact. A simple test of such a system is to see how well it fares on
modeling a situation such as the following: Tom pities his friend Harry who is sad because his (Harry's) mother has had her garage
burn down. Humans have no trouble with this because not only do we have an
internal representation of our own concerns, but we are also apparently able
to use some similar mechanism for representing the concerns of others, and
thus ``imagine'' how they might feel with respect to some situation. In this
case Tom must not only model Harry, but he must also model
the concerns that Harry is modeling for his mother. (See
section 2.8.3.)
The Affective Reasoner, in contrast to ACRES, incorporates
representations of the concerns of others in its architecture (see section
2.8), and represents relationships between agents. These
representations are incorporated into the emotion-generation process when
initiating fortunes-of-others emotions (see section 2.3) and into
the process that helps agents identify new situations as instances of a
certain emotion type.
The System for Interpretation of Emotional Situations (SIES) of
Rollenhagen [24], and the AMAL system of O'Rorke
[16] share a number of features. They both have taken
on the burden of
having to do non-derivative reasoning about the situations that give rise to
emotions within the object domain. They both use retrospective reports about
emotion-inducing situations as the basis for the structural content analysis
that drives the reasoning systems. Lastly, they both are descriptive in
nature. SIES attempts to better define emotion terms by exploring their
similarities and differences through abstract representations. AMAL attempts
to explain situations as emotion episodes through the use of abductive
reasoning.
By contrast, the character of the Affective Reasoner, as a simulation, is quite different from these two programs. In the Affective
Reasoner the process of representing emotions contributes not only to
developing a language for describing emotion episodes but also to
the building of intelligent agents that ``have'' emotions. In addition,
while the Affective Reasoner uses a logic-based representation for reasoning
about the eliciting conditions for emotions, similar to both SIES and AMAL,
neither of these latter systems has anything like the case-based approach
used in the Affective Reasoner to reason backwards from the manifestations
of emotions to the emotions themselves.
The theoretical underpinnings of AMAL and SIES are quite different,
although in practice this difference is minimized. AMAL (based in part on
the work of Ortony, et al. [18]) is an extension of the cognitive appraisal approach where the antecedents of emotions are
described independently of the particular situations from which they derive.
In this approach it is situation types that give rise to emotions
(e.g., an undesirable event giving rise to distress), rather than relatively
specific situations (such as the death of a loved one). However, since AMAL
is designed to be able to represent such real-world emotion-eliciting
situations as those portrayed in student diaries [31],
it must also be able to do extensive reasoning within the object domain. To
represent these emotion anecdotes a situation calculus is used.
SIES, on the other hand, is an attempt to combine the cognitive approach
with a situational approach in which the antecedents of emotions are
seen as arising in certain real-world situations (e.g., loud noises leading
to fear, or separation from a loved one through death leading to sadness).
In his description of SIES, Rollenhagen gives many rules for emotion
reasoning, but most of these are employed in describing an instance of a
given emotion eliciting condition as embodied in a narrative description -
that is, in giving structure to the derivative motivations of the agents.
SIES should be understood as a system for analyzing emotion
episodes. In this regard, much of the contribution of the system is in
structuring the representation of the data. The program does not map
situation features into specific eliciting conditions. Instead, inferences
tying abstract situations into the personal concerns of the subjects are
made at the time the researcher encodes each emotion eliciting situation.
The representation of emotion eliciting situations in the Affective
Reasoner is from a different perspective, and is structured around the idea
that agents may be seen as carrying internal schemas that match
situations that arise in their world. Here the focus is on frames that
contain enough information to discriminate one situation from another
through matching. Varied interpretations of these situations,
produced by unifying an interpretation frame with a situation frame during
the course of the simulation, are seen as whole units. These interpretation
units are configured in various ways such that they give rise to the various
emotions.
Unlike SIES and the Affective Reasoner, AMAL [16] does do
extensive, automated, plausible reasoning about derivative motivators. To
do this it uses an abduction engine. AMAL uses a logic-based situation
calculus framework to describe actual emotion
episodes[31]. Using abductive reasoning, AMAL is able to
construct plausible explanations for emotion instances. For example AMAL
analyzes plausible relationships in the following story:
One other important difference among the three systems should be
noted. AMAL and SIES typify the approach that takes the logical structure
of the preconditions for emotions to be simply an extension of the structure
of the world (e.g., shouting causes Tom to startle, which causes him to drop
a hammer on his foot, which causes him to feel pain, which causes his friend
to feel sorry for him). Our approach is fundamentally different in that
there is a definite demarcation between the internal strong-theory rules
used to generate emotions for agents, and the object-domain rules used to
map into them. In this approach, since we see emotion eliciting situations
in the object domain as instances that may or may not match internal schemas
representing the concerns of the agents, processing and data-entry tasks are
very different in nature. While each approach can, of course, be reduced to
a logic representation, the two languages for content theory representation
are entirely different, and thus, in practice, so are the systems. In AMAL
and SIES everything is represented as rules, in the Affective Reasoner the
concerns of the users are represented as collections of features.
Toda's work on Solitary Fungus-Eaters [30] attempts
to describe emotions as a situated functional component of society. In this
case, Toda's society is that of simple (fantasy) uranium-mining robots that
operate autonomously on a distant planet and eat native fungus to survive.
The goal of these robots is to collect as much uranium as possible. If they
consistently choose only to travel to locations containing uranium, however,
they may not encounter enough fungus to keep themselves going and in such a
case would ``die''. Society arises out of a need for a ``coalitional
bond'' to help protect the robots against stronger predators, and so forth.
Toda's structural analysis of the human emotion system is based on
his stated belief that emotion was a useful mechanism for a very different,
primitive, society than our current one, and that just as our bodies have
problems dealing with modern-day environmental stresses such as pollution,
so does our emotional system have difficulties with such an (evolutionarily)
unfamiliar environment. To illustrate his ideas about why an emotion system
arose in the first place, giving insight into its nature, Toda details a
system of urges necessary to solve coordination problems in his
simple robot society.
For example, in response to the threat of a larger predator, a
Fungus-eater may have a Fear Urge which causes him to send out a
distress signal. This in turn triggers a Rescue Urge in other robots
who then come to jointly attack the predator. Since a reward is necessary
for this scenario to fit into the ``individual-gain'' paradigm of Toda's
robot society, a Gratitude Urge is developed. Rewards, given out of
gratitude by the rescued robot, may have to be postponed, however, if the
rescued robot is short on resources. Since this delay may cause problems, a
more immediate reward in the form of a Love Urge is developed, and
so forth.
This major difference between this work and that done on the
Affective Reasoner is of course that the latter is actually implemented as a
running program. While Toda has given some level of detail for many aspects
of his system (including vision and planning, not discussed here), there are
nonetheless many gray areas in his description. Vision and planning aside,
such social actions as defecting from one's coalition partner and joining a
more profitable coalition are treated as primitives in the discussion. To
implement even a small number of these primitives of the robot society would
be a major task. In addition Toda's urges, intended to be thought of
tentatively as simple emotions, are presented anecdotally and not as part of
a cohesive, complete theory.
Toda's theoretical perspective shares with both the Affective
Reasoner and ACRES the idea of emotions as mediators for controlled,
immediate responses to situations (see also [27]). To this
end, emotions take a functional role in the automated organism.
From this short survey it should be apparent that comparatively
little work has been done in this area, and that what has
been done is often so different from what has come before that comparisons
are strained. PARRY is a system that attempts to model a diseased
and limited mind. BORIS, OpEd and THUNDER use a content theory of emotions
to aid in the understanding of text. ACRES attempts to model the dynamic
utility of emotions. SIES provides a structure for mapping from the features
of situations into the emotions. AMAL uses abductive reasoning, well suited
to the understanding of emotion episodes, to explain emotion situations.
Clearly these are diverse approaches only
loosely tied together by the generic categorization of the work as ``emotion
research''. One theme that all of these treatments share however,
is that working out theories at the level of detail required for a running
computer program forces the theorist to refine and completely specify all of
the hidden ``gray areas'' of the theory. This is particularly important for
this complex, and little understood subject.
1.4 TaxiWorld
The Affective Reasoner has three primary components: (1) the
affective reasoning component, which is independent of any domain, (2) a
world simulation based on an object-domain theory, and (3) a graphics
interface based on the simulated world. Once an object-domain theory has
been incorporated into the Affective Reasoner, and a graphics interface
customized for that domain, it becomes a domain-specific system. In its
current form the Affective Reasoner primarily manipulates agents that
are intended to be interpreted as operating in a schematic representation
of the Chicago area (figure 1.1). We call this TaxiWorld.
In fact, other partially analogous domains can be simulated if the user
simply reinterprets the meaning of the icons displayed. This how the
football game example discussed in chapter 3 was run.
TaxiWorld has approximately forty-five different sets of simulation
events which produce emotion eliciting situations. Included are events that
represent traffic accidents, rush hour delays, getting speeding tickets,
picking up passengers, getting paid, having to wait a long time for a taxi,
and so forth. In addition, there are approximately forty different
rudimentary ``personalities'' which may be given to each agent that
participates in one of these situations. For example, one taxi driver may
get angry when he is given a small tip whereas another may just figure it is
part of the job. Or, both may get angry but one will be rude to his
passenger, whereas another may just smile and pretend that he does not care.
There are roughly 150 different candidate interpretations of the forty-five
situations. Different interpretations, or sets of interpretations,
can give rise to one or more of twenty-four emotion types, each of which
has about sixty possible action responses associated with it. Used in
different combinations these components can yield tens of thousands of
different emotion episodes.
In any research of this kind the question naturally arises as to
what has actually been implemented and what has been run. In this
dissertation only a few emotion episodes will be discussed in detail so as
to allow us to focus on emotion representation issues. We provide at most
one emotion episode example for each point. However, it should be understood
that many different examples may actually have been run in the process of
addressing any one particular problem. In addition, unless otherwise stated,
illustrative examples in this dissertation are based on code that has been
written and actual simulated episodes.
The researcher using TaxiWorld has two forms of input into the
system. First he or she statically configures the object domain (i.e., the
world of taxi drivers and their passengers) through LISP files. Second, he
or she dynamically manipulates the running simulation through menus and
windows. Configuring the object domain has six parts: (1) Should the
researcher wish to extend the number of situations represented then new
simulation events must be entered into a LISP file, and event handlers
written to process them. (2) Once the object domain (e.g., the world of taxi
drivers) is stable, the researcher may choose to add new interpretations of
the situations that arise in it by creating construal frames (see
chapter 2) with which agents interpret situations. (3)
New emotion manifestations may be created within the object domain. For
example, in the world of taxi drivers we may wish to represent ``cutting
someone off'' as an expression of anger. (4) New personalities may be
created by grouping (old and/or new) construal frames into sets, and by
grouping temperament traits (i.e., tendencies toward certain types
of actions as expressions of emotion) into new sets. (5) Simulation
sets may be constructed by specifying which of the represented situations
are to arise, which types agents are going to be present, and which
personalities those agents will have. Lastly, (6) a content theory for
reasoning about observed actions from cases may be recorded in a data file
or through interaction with the running system. User input macros have been
provided for (2) - (6).
Figure 1.1 shows a portion of TaxiWorld's map display.
Interaction with TaxiWorld is through this interface. Except for stop and go each of the buttons on the control panel leads to a
pop-up menu for manipulating different aspects of the system. This control
mechanism allows the following functions, by button: (1) stop and
go: start the simulation running or pause it, (2) demos:
select a configuration of agents and situations for the simulation, (3) control: change the speed of the simulation, or enable and disable
different features in the simulation such as whether or not agents
negotiate and what types of traffic delays may arise, (4) set: set
rudimentary global moods for the agents, and set the mode for the heuristic
classification system (i.e., set the case-based reasoning system in learn mode, in report-only mode, or off), and (5) queries: look at a scrolling window containing summaries of the
situations that have arisen so far.
Figure 1.1:
The TaxiWorld display.
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Once the user has configured and started the simulation, the
animated icons move around the screen as agents who ``experience'' the
situations in the simulated world. The figure shows a simulation that has
been running for a short time. Four taxi drivers, Tom, Dick, Harry and Sam
are shown. Tom and Sam are traveling between Chicago's O'Hare Airport and
the Loop, Dick is between the ``Junction'' and the Loop, and Harry is on his
way to the Loop from the Museum of Science and Industry (not pictured). One
passenger is waiting for a ride at Northwestern University, and five
passengers are queued up waiting for rides from the Chicago Botanical
Gardens. During rush hours the roads swell with traffic and the agents move
more slowly. This occurs also with accidents. Highway patrol agents may
also be displayed.
Selecting an agent with the mouse provides information about that
agent. One option will bring up a scrolling window containing the current
``physical'' state of the agent: where he is, whether he has someone in the
cab (if he is a taxi driver agent), how much gas he has left, where he is
going, etc. Another option brings up an emotion-history scrolling window.
Figure 1.2 shows one of these, in this case for Harry. Here we
see summaries of two emotion instances, distress and dislike. These have arisen from two different construals of the same
situation. Briefly, a new passenger has gotten into Harry's cab and was
cheerful. Harry, whose ``personality'' type might be roughly described as
depressed grouch has a goal and a preference that apply in such a
case. First, he has no desire to be around happy people because they remind
him of how unhappy he himself is. This leads to distress. Second, he just does
not like happy people, he finds them distasteful, so he dislikes this
passenger.
Figure 1.2:
The emotion history scrolling window.
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For maximum flexibility, the researcher has the option of
communicating with the running system directly through LISP. Basic tools
have been provided for this, in particular to control the asynchronous
processes spawned in the simulation. The user can also interact with the
case-based reasoner through LISP as well.
TaxiWorld is written in Common LISP and runs on a SUN SPARC
station 1 with 40MB of RAM. It uses a simple SOLO graphics
interface. The TaxiWorld code is about fifteen thousand lines in length,
and is combined with an additional fifteen thousand lines of slightly
modified code based on Dvorak's Common LISP version of Protos
[7].
1.5 Two examples from TaxiWorld
Figure 1.3:
Harry learns about Tom's fear.
 |
In this section we look at two examples of the kinds of episodes
that occur in TaxiWorld. Figure 1.3 represents a simple episode
where a taxi driver, Harry, observes another taxi driver, Tom, and learns
something about his fears. This episode makes use of three agents (the third
is Tom's passenger - not pictured), one situation (that arises in the
Chicago area) and a single affective state, in a TaxiWorld
simulation. In this episode we see the following: (1) Tom picks up a
passenger headed for Joliet at the Museum of Science and Industry. (2) The
passenger is seedy-looking, causing Tom to fear that he will be robbed. (3)
This fear is expressed as a flushed expression on Tom's face, a stutter in
his voice, and the statement that he does not have enough gas to drive to
Joliet. (4) Another taxi driver, Harry, watches this episode. He does not
know that Tom is inclined to be afraid of seedy-looking passengers whose
destinations are Joliet. He has no representation of how Tom might construe
such an event. However, (5) Harry has seen a case where another agent
had a flushed face and a stutter in his voice. The previous case was known
to be an example of fear. (6) Harry now reasons to come up with a
good explanation: if Tom is experiencing fear, why might this be so? He
``imagines'' some possible interpretations of this event that might cause
someone to be afraid and reasons that if Tom had the goal of retaining his
money and/or maintaining his personal safety, then given that seedy-looking
people have been known to rob cab drivers - thus taking away that money
and perhaps causing harm, Tom might experience fear. Since this explanation
fits, Harry drops his search. (7) He updates his internal representation of
Tom as someone for whom retaining money is important, and who is afraid of
unsavory passengers. (8) Harry also now has a new case which he may wish to
save. The ``I don't have enough gas'' ploy, which was not a feature of the
previous case of fear, may be explained to him as a problem-reduction strategy, or it may be left unexplained but associated,
depending on whether the system was being trained, or was running in
automatic learning mode.
Harry now believes he has learned something new about Tom. If this
knowledge is correct he will be better able to explain Tom's future actions
in response to such a situation without resorting to a case search (i.e., he
knows how Tom will interpret the situation). In addition he will be
able to predict how Tom might react to such an event, even in the
absence of any empirical evidence. On the other hand, if at a later time the
knowledge proves to be erroneous it will be discarded and a search for an
alternate explanation will be initiated.
Figure 1.4:
Negotiating about who takes the seedy-looking passenger to Joliet.
 |
The episode pictured in Figure 1.4 expands on the previous
example. Here we see that some time later Harry has come to know Tom quite
well.![[*]](footnote.png)
He has met a new agent, Dick, who seems to respond to events the way Tom
does, although he (Harry) has never seen Dick pick up a seedy-looking
passenger headed for Joliet. Until he knows differently, Harry makes the
assumption that Dick is just like Tom (i.e., with respect to the way he
interprets situations). At one point, Harry (who has the goal of making
money and likes to go to Joliet because, as a long trip, this furthers his
goal), sees an opportunity. Here is the sequence of events: (1) Dick and
Harry arrive at the Botanic Gardens in that order, giving Dick the right to
the first passenger. (2) A seedy-looking passenger arrives at the cab stand
and wants to go to Joliet. (3) Harry matches the passenger-arrival
situation against an opportunity prototype.He sees an opportunity to make a request of Dick, but ``thinks it through''
first to see if it is feasible. Using his representation of how Tom sees
the world as a default for Dick, Harry believes that Dick, like Tom, has
fear about going to Joliet with the seedy-looking passenger. On the other hand
he believes that he will, himself, be happy about going on such a trip. (4)
Harry proposes to Dick that Harry, instead, take the fare to Joliet. (5)
For his part, Dick, considering this proposal, ``imagines'' that Harry gets
to take the passenger. He has no emotional response to this. Next he
``imagines'' taking the passenger himself. As it happens, Harry's schema
for Dick is incorrect: while Dick is indeed very much like Tom in many ways,
he is unlike him in that he is not afraid of seedy passengers who want to go
to Joliet. Consequently Dick does not see a threat to his goals, but rather
only that if he does make the trip to Joliet he will make some
money. In other words, he reasons that if he makes the trip he will be
happy, and that if he does not make the trip he will simply experience a
lack of emotion. (6) He therefore does not agree to Harry's proposal and
instead takes the passenger himself. (7) Harry believes he has learned
something new about Dick. Apparently Dick does not construe seedy
passengers going to Joliet as threatening. Harry splits off his internal
representation for the goals, standards and preferences of Dick from the
representation of those for Tom and removes the offending interpretation
frame from the latter.
In this example we see that Tom and Dick both have distinct
emotional lives, and that Harry is able to reason about them. Tom has a goal of making (keeping) money and is able to experience fear as a
result of believing that this goal may be threatened. Similarly, Dick also
has a goal of making money and is capable of feeling hope over
the prospect of taking a passenger on a long trip.
On the other hand, Harry, who is observing Tom and Dick, draws on
his experience, and his internal schemas for the other agents, to explain
their actions. In the first case, he has no clue as to how Tom might
interpret the situation. He looks through some cases to see if he might make
a guess as to what stuttering, flushing and the out-of-gas ploy
might indicate. He has seen something similar when someone else was afraid,
and he can explain the fear as a fear of getting robbed. Since this
explanation of the situation is workable he assumes that this is part of how
Tom interprets the world, and he now adds this to his representation of
Tom. Until he has cause to do differently he will now always make this
assumption about Tom.
In the future, Harry will test this knowledge whenever a similar
situation arises by asking this question, Given a similar emotion
eliciting situation (seedy passenger going to Joliet), does the observed
agent respond in a manner compatible with an emotion to which the assumed
interpretation leads? If the agent does, then Harry will be more
confident that his assumed interpretation is correct; if he does not, then
Harry will have to search for another explanation (i.e., one which leads to
a different emotion).
In addition, Harry's representation of Tom is one he can draw on as
a default personality type, for reasoning about a new agent, Dick. As
long as Dick's actions can be explained in terms of Tom's personality, the
default personality suffices and he uses it to ``see the world through
Dick's eyes''. When an opportunity arises he believes that it is worth
pursuing because he is able to ``imagine'' how Dick will perceive his offer.
However, when Dick turns him down, Harry reasons that he has made an
incorrect assumption about Dick and removes this supposition from his
representation of how Dick sees the world. But this means that Dick no
longer looks just like Tom, so Harry must split the two schemas apart. The
old one still represents Tom, and the new one now represents Dick.
The rudimentary negotiation, the simple opportunity recognition, and
the rudimentary default personality reasoning, all open research questions
in their own right, are not what is important in these examples. What is of
interest is the idea that there are units of appraisal, here represented as
frames, which can be used as filters for the interpretation of situations,
and that these appraisal units can be mixed and matched as necessary to
construct rudimentary personalities for agents (i.e., to give them primitive
affective life). Of additional interest is the idea that these appraisal
units can also be used by an observing agent to construct an internal
representation of the observed agents for generating explanations of
their actions, and for predicting their emotional responses to future
similar situations. Lastly, it is important to consider that agents
manifest their emotions in ways that can be understood by observers: agents
often communicate their emotions through their actions, or their lack
thereof.
In the following chapters we attempt to illustrate some concerns
that have to be addressed by emotion reasoning systems, how our
representation addresses these concerns, and how it can be used to model a
number of different multi-agent interactions.
2 Overview
In this chapter we provide an overview of the Affective Reasoner.
The discussion focuses on the functional role of the various modules in the
system. Two of these modules (construal, and action-generation), are quite
elaborate, having important theoretical content, and so are only introduced
here since they are discussed more fully in later chapters. The other
modules are less elaborate, and are treated fully in this overview.
The sections are organized around the various stages the system goes
through in processing a simulated situation. They are introduced in the
order of the different processing stages. These stages, and the static
representations which they use, are illustrated in figure 2.1.
Processing flows from some initiating simulation event through emotion and
action generation to the final stages simulating observation by other
agents. Roughly speaking, this may be interpreted as follows: Something
happens in the modeled ``world'',
creating a situation. The agents that populate this world interpret the
situation in terms of their individual concerns. Each interpretation is
reduced to a nine-attribute relation, and some variable bindings. Specific
configurations of the relation lead to the production of specific emotional
states in the agents (i.e., their emotional responses to the situation).
These emotional states, in turn, are manifested as action responses. Agents
can observe each other's responses to situations and attempt to explain
them in terms of emotions the other agent may have been
experiencing. An observing agent then finds an explanation for that emotion
based on varied interpretations of the original situation. Once this is
done, the observer places the schema that lead to the successful
interpretation (i.e., the interpretation that lead to what was believed to
be the other agent's emotion) into a database representing the concerns of
the other agent. This Concerns-of-Other database may then be used to predict
and explain the other agent's responses to future similar situations.
Figure 2.1:
Processing stages and related representations
 |
2.1 Fundamental concepts
This research is at the juncture of Artificial Intelligence and
Cognitive Science. Its interdisciplinary nature can lead to confusion over
the use of terms, such as personality. Accordingly we will, in this
section, make explicit our intended meanings. In addition, we also discuss
some central concepts, such as the basic emotion eliciting condition theory
from
[18] on which the construal process is based.
Rudimentary personalities. To computer scientists personality means something like an aggregate of qualities that
distinguish one as a person. (Webster). To psychologists however, personality means something rather different, something like co-occurring classes of trans-situational stable traits.
In the Affective Reasoner, the agents in the system have rudimentary
``personalities'' (at least computer scientists might consider this to be
so) that distinguish them from one another. These rudimentary personalities
are divided into two parts: the disposition agents have for interpreting
situations in their world with respect to their concerns, and the
temperament that leads them to express their emotions in certain ways. We
have chosen the terms interpretive personality component and manifestative personality component to denote these two parts of an agent's
unique makeup. By the former we mean the rudimentary
``personality'' which gives agents individuality with respect to their
interpretations of situations (i.e., their uniquely individual concerns).
By the latter we mean the rudimentary ``personality'' which
gives agents individuality with respect to the way they express or manifest
their emotions.
Situations that lead to emotions. In the Affective Reasoner,
some simulation events create situations that can initiate emotion
processing on the part of the agents involved. These we call emotion
eliciting situations or simply, eliciting situations. An example of
such an eliciting situation is the conceptual ``arrival'' of some
agent at a location, which might give rise to an emotion such as relief or
distress. Eliciting situations are not to be confused with eliciting conditions which derive from the emotion eliciting condition
theory of [18] and are discussed below.
Goals. This term has a very broad meaning. Here we use it in
its simplest sense: a state of affairs that an agent desires to have come
about. In this dissertation, unless otherwise specified, most goals will be
considered to be equivalent in structure.Clearly this is not actually the case. Some goals are preservation goals,
some are never satisfied (i.e., living a good life), some can be partially
achieved by achieving some other goal (i.e., saving another $100 towards a
house), and so forth. In general, however, these distinctions have more to
do with goal generation, interaction and retirement than they do with the
way goals fit into our underlying cognitive theory. Consequently, for most
of the discussion it will suffice to define the term goal to mean
simply, a desired state of affairs that, should it obtain, would be
assessed as somehow beneficial to the agent.
Object domain. Emotion reasoning can be performed as well by
one person as by another. Furthermore, people can have emotions about almost
anything and in almost any circumstance. People have emotions about
something important like the state of their finances, but they might also
have them about something as apparently inconsequential as the state of
their shoelaces. For this reason emotions may be considered an abstract domain that operates within object domains. The language of the
emotion domain is abstract: goals, standards, preferences, and so forth. The
language of an object domain is specific: money, shoelaces, etc. The
Affective Reasoner can be used to do abstract emotion reasoning in any
object domain that can be modeled. One such object domain is the world of
taxi drivers, as represented by the TaxiWorld version of the Affective
Reasoner. By object domain then we mean, that domain in which
the eliciting situations for emotions are described, and in which
emotion-based actions are manifested.
The emotion eliciting condition theory. The emotion eliciting
condition rules we use for the strong-theory reasoning component of the
Affective Reasoner are based on the work of [18]. Ortony, et al.
specify twenty-two emotion types based on valenced reactions to
situations construed either as being either goal-relevant events,
acts of accountable agents, or attractive and unattractive objects. The extended and adapted twenty-four emotion-type
version of the emotion eliciting condition theory that we have used in the
Affective Reasoner is outlined in table 2.1, based on
the work of [16]. Each of these twenty-four emotion states has a
set of eliciting conditions. When the eliciting conditions are met, and
various thresholds have been crossed, corresponding emotions result. A key
element of the theory is that the way emotion eliciting situations map into
these eliciting conditions depends on the interpretations of the
individual agent.
basket at the buzzer, and your team loses. I may experience joy at the
event, whereas you may experience distress. In both cases we share the
same sets of eliciting conditions for our emotions and the emotion eliciting
situation is the same (i.e., the ball went in the basket just before the
buzzer); it is only the interpretation or construal of the situation
which is different.
EMOTION CATEGORIES (TYPES)
Clark Elliott, 1998
after Ortony, et al., 1988
Table 2.1
|
GROUP
|
SPECIFICATION
|
CATEGORY LABEL AND EMOTION TYPE
|
| Well-Being |
appraisal of a situation as an event |
joy: pleased about an event
distress: displeased about an event |
| Fortunes-of-Others |
presumed value of a situation as an
event affecting another |
happy-for: pleased about an event desirable for another
gloating: pleased about an event undesirable for another
resentment: displeased about an event desirable for another
jealousy*: resentment over a desired mutually exclusive
goal
envy*: resentment over a desired non-exclusive goal
sorry-for: displeased about an event undesirable for
another |
| Prospect-based |
appraisal of a situation as a prospective
event |
hope: pleased about a prospective desirable event
fear: displeased about a prospective undesirable event |
| Confirmation |
appraisal of a situation as confirming
or disconfirming an expectation |
satisfaction: pleased about a confirmed desirable event
relief: pleased about a disconfirmed undesirable event
fears-confirmed: displeased about a confirmed undesirable event
disappointment: displeased about a disconfirmed desirable
event |
| Attribution |
appraisal of a situation as an accountable
act of some agent |
pride: approving of one's own act
admiration: approving of another's act
shame: disapproving of one's own act
reproach: disapproving of another's act |
| Attraction |
appraisal of a situation as containing
an attractive or unattractive object |
liking: finding an object appealing
disliking: finding an object unappealing |
Well-being/
Attribution |
compound emotions |
gratitude: admiration+joy
anger: reproach+distress
gratification: pride+joy
remorse: shame+distress |
Attraction/
Attribution |
compound emotion extensions |
love:admiration+liking
hate:reproach+disliking |
*Non-symmetric additions necessary for some stories.
The emotion types are simply categorizations of selected patterns of
emotion eliciting conditions. They have been given English names roughly
corresponding to an intensity-neutral label for the type of
emotions represented by the specific configurations of the emotion
eliciting conditions. It is important to note that these names (e.g., joy and anger) given to the emotion types are not to be
mistaken for the specific emotions to which they usually refer. For
example, annoyance is one of the anger type emotions, as is
rage, because they both follow from interpretations of a situation
as an undesirable event coming about as a result of someone else's
blameworthy act.
Emotion eliciting conditions leading to emotions
fall into four major categories: those rooted in the effect of events on the goals of an agent, those rooted in the standards
and principles invoked by an accountable act of some agent, those
rooted in tastes and preferences with respect to objects
(including other agents treated as objects), and lastly, selected combinations
of the other three categories. Another way to view these categories is as
being rooted in an agent's assessment of the desirability of some
event, of the praiseworthiness of some act, of the attractiveness of some object, or of selected combinations of these
assessments.
Emotion types. As discussed above, when we speak of an
emotion generated by the system we are really talking about an emotion of
that type. It is usually not convenient (i.e., it obscures the
meaning of the text) to talk about the ``emotion type of anger'' and so
forth. However, it should be understood that throughout this text that when
we refer to some emotion, as though by name, we are actually referring to
some unspecified emotion characterized by that named emotion type.
Goals, standards and preferences databases. These
are referred to in the text as GPSs. They are the hierarchical frame
databases used to represent an agent's concern structure.
They are organized around the
three categories of an agent's concerns. They hold most of the information
used to define an agent's interpretive personality component. When
an agent is created, the GSP database must be filled in to give it a
unique set of concerns. When an eliciting situation is interpreted using
this database, attributes of the eliciting condition theory are bound to
features in the situation. See table 2.1, and
chapter 3 for discussion.
Representing concerns of others. An agent may represent the
concerns of some other agent by keeping a partial GSP database for that
other agent, and filling it in as knowledge about the agent is acquired.
Since these databases represent the concerns of other agents, with respect
to an observing agent, they are known as Concerns-of-Others,
or COO, databases.
Since they are generally learned by the agent, COOs are usually incomplete,
and may contain erroneous interpretation schemas as well. See
section 2.8 for a discussion.
Emotion Eliciting Condition Relations. The process that
matches frames in the GSP (and COO) databases against an eliciting situation
will, if the match is successful, reduce the eliciting situation to a set of
bindings. Some of the bindings represent values for two or more of the nine
attributes of a special relation known as the emotion eliciting
condition relation.
Different patterns of bindings for the attributes in this relation, and
different values, give rise to different emotions. In the text these are
abbreviated as EEC relations. See section 2.3 for
discussion.
Action response categories. Once an agent is in an emotional
state, it will manifest this emotion in one way or another. Some of these
manifestations naturally fall into one functional category, while others
fall into another. For example, trembling and breaking into a
cold sweat may be categorized by the somatic characteristics they
share. We have specified approximately twenty different groups (there is
some variation between the emotions) to which the various action responses
belong. See figure 4.1 for a complete listing of the action
response categories for gloating, and section 2.6.1 and
chapter 4 for discussion.
Frame types. Construal frames, which interpret emotion
eliciting situations (also represented as frames) must be retrieved from an
agent's GSP database at the time the situations arise. The retrieval of
candidate construal forms is a feature-indexing problem. A satisfactory
solution to this problem is beyond the scope of the current research. We
sidestep it in the Affective Reasoner by simply giving each eliciting
situation a type, such as an arrival-at-destination frame
type, or a passenger-pays-driver frame type. Construal frames of a
particular type are candidates for interpreting eliciting situations of the
same type.
The structure of simulation events. The Affective Reasoner is
designed around a simulation engine. Initial simulation events are
placed in the system's event queue. When the simulation is started these
simulation events are processed, and spawn further simulation
events which are then placed in the event queue, ad infinitum. In this
manner, once the simulation is started, it continues until it is halted.
Some of the simulation events simply drive the system, moving icons around
the screen and so forth, and are not of theoretical interest. Other
simulation events have eliciting situation frames attached to them
which, when matched against the concerns of agents, can initiate emotion
processing.
2.2 The construal process
The construal process is covered in detail in
chapter 3 . Here we give a brief introduction to the
emotion eliciting condition theory from [18], and place the
construal process in the context of that theory. After this introduction we
discuss how a matcher is used to identify and instantiate internal schemas
called construal frames which are used to interpret eliciting
situations in the simulated world for the simulated agents.
When a simulation event occurs which has an attached eliciting
situation frame, agents appraise the situation's relevance to their
concerns by trying to match it against internal schemas representing the
eliciting conditions for the various emotions. Has a goal been achieved?
If so, is the goal important enough to generate an emotion? Has a standard
been violated? Who is responsible for the blameworthy act? The answers to
such questions result in an interpretation of the event with respect
to the eliciting conditions for emotions in terms of the observing agent's
concerns. If the interpretation is such that the eliciting conditions have
been met, and certain feature thresholds have been reached (e.g., was enough money lost...), then an emotion will result.
The internal schemas which map emotion eliciting situations into the
eliciting conditions for an emotion for some agent are represented as
frames, known as construal frames. These construal frames are in
an inheritance hierarchy. Leaf node frames inherit slots from ancestors, so
that many attributes need only be specified once, in some ancestor.
The frame system is based on XRL [4] but has been extended so that
slots may contain pattern-matching variables and attached procedures. Since
variables allow us to create generalized frames, many different instances of
a certain situation type may match the same construal frame, albeit with a
different set of resultant bindings for the variables produced by the match.
Figure 2.2 shows how inherited slots are used to match
eliciting situation frames. In this figure the slots 
through 
come
from three different frames. Frame 3 is the leaf node construal frame,
and should be thought of, conceptually, as containing all five slots. These
five slots match the slots for the eliciting situation, pictured at the
bottom of the illustration. Note that the hierarchical nature of the GSP
database is purely for the convenience of capturing an agent's concerns in
frames. There is no functional reason for the hierarchy (i.e., there is no
run-time processing dependent upon the hierarchy); the hierarchies of frames
could be compiled by collapsing them into ``flat'' representations of the
leaf node frames, so that inherited slots would be propagated down and
included in each of the containing frame's leaf-node descendants.
Figure 2.3:
Inheritance of slots in a construal frame
 |
Once candidate frames have been selected as potential matches for an
eliciting situation (using the situation frame type as discussed above), the
matcher comes in to play. This matcher uses a specialized unification
algorithm, and is discussed in chapter 3. If a match
between the emotion eliciting situation frame and a particular construal
frame succeeds then the eliciting situation is considered to be of concern
to the agent, as interpreted by the construal frame. At the same time, a
set of bindings is produced from the unification of the pattern-matching
variables and the features of the situation, and any attached procedures
(see below). The feature values contained in these bindings, and
specifications within the construal frame, are used to determine the exact
nature of the eliciting situation with respect to the agent's concerns. In
addition, these bindings are later used to specify the
possible responses as well. Output from the match process is either an
indication that the match has failed, or an instantiated construal frame
containing bindings for eliciting condition attributes such as desirability
for the agent, praiseworthiness of the act, attractiveness of the object,
and so on.
In figure 2.3, Frame 2 is shown with a
procedure attached to slot 
. Such procedures allow working memory
values to be incorporated into the match process. They also allow
fine tuning of the match process by calling predicate functions
which may base decisions on the current set of bindings. Finally,
these attached procedures may also contribute to the current set
of bindings, since the (new) set of bindings is returned from the
procedure calls.
Frame 1, slot A and Frame 3, slot D are shown as
sharing a common variable ?z. In the eliciting situation, this
feature is instantiated as 
in slots A' and D'. During
the match, since unifies with , this portion of the
unification process succeeds and a binding of the variable ?z to the
constant is created and added to the bindings list.
An important point to note is that within this context, no
event has meaning to an agent until after it has been filtered through the
concerns of that agent. Without going into the philosophical
foundations of this argument
(but see [22] for a discussion of this) it should be evident
that people work this way: extraneous cognitive and perceptual information
is filtered out of the input stream, and the relevant situations that do
pass this filter are sent on, along with the interpretation of why
they are relevant. Not all interpretations are relevant to the human
affective machinery (i.e., the information that a room is dark and that the
light needs to be turned on is not likely to cause an emotional response),
but a significant amount of binding to affective inference structures occurs
at the time eliciting situations are assessed. For example, affective
states are intertwined with expectations. To form a match between a stored
expectation and some current situation for the purpose of (dis)confirming
the expectation, one must bind features of the new situation to the expected
facilitation or blocking of the stored goals, to the expected upholding or
violation of the stored standards, and so forth.
2.3 Emotion Eliciting Condition relations
Once an initial interpretation has been made, Emotion Eliciting
Condition (EEC) relations are constructed. These are, essentially, a set
of features derived from eliciting situations and their interpretation
which, taken as a whole, may meet the eliciting conditions for one or more
emotions. Different patterns of features lead to different emotions. The
source of these features varies. Some features, such as the names of agents,
are directly derived from the situation. Other features, such as the desirability of the event, are derived from matching the situation against
the agent's concern structure (i.e., they are contained in the bindings
produced when a construal frame from the agent's GSPs is matched against the
situation frame); still other features, namely pleasingness and status, are dependent upon dynamic information and so must be derived
partially from working memory. The complete set of features comprising the
EEC relation is shown in figure 2.4, and is discussed
below:
Figure 2.4:
The Emotion Eliciting Condition Relation
| self |
other |
desire-self |
desire-other |
pleasingness |
status |
evaluation |
responsible agent |
apealingness |
| (*) |
(*) |
(d/u) |
(d/u) |
(p/d) |
(u/c/d) |
(p/b) |
(*) |
(a/u) |
| Key to attribute values |
| abbreviation |
meaning |
* |
some agent's name |
d/u |
desirable or undesirable (event) |
p/d |
pleased or displeased about another's fortunes (event) |
p/b |
praiseworthy or blameworthy (act) |
a/u |
appealing or unappealing (object) |
u/c/d |
unconfirmed / confirmed, or disconfirmed |
- Self. This attribute represents the identity of the
agent experiencing the emotion. The value for this attribute is derived from
the situation. It is always bound to an agent's name. If two agents are
involved in some situation, and it is relevant to the concerns of each of
them, then two sets of emotion eliciting condition relations will be
produced, with self bound to a different agent in each of the
different sets. Three agents will yield three sets, and so on.
- Other. This attribute represents the identity of some
other agent about whose fortunes the self agent may have an emotion.
Such an emotion will result only when pleasingness (for self
- see below) is also bound, on the basis of a relationship stored in
working memory. The value for this attribute is derived from the situation.
When the attribute is bound, it is always bound to an agent's name, but it
is never the same value as self.
- Desire-self.
This attribute represents the self agent's assessment of the
eliciting situation as an event. If he interprets the situation as one
where a goal of his is blocked then the value of this attribute is undesirable; if he interprets it as one where a goal is achieved
the value is desirable. If he does not interpret the eliciting
situation as relevant to its goals then this attribute is not bound. The
value for this attribute is derived from the agent's construal of the
eliciting situation.
- Desire-other.
This attribute represents the self agent's assessment of the eliciting
situation as an event relevant to the desires of another agent. If a self agent has a Concerns-of-Other representation for some other agent
(either specific or default - see section 2.8), he can interpret
that agent's reaction to a situation by ``imagining'' what it is like for
the other agent. If it is perceived that a goal of the other agent is
blocked the value of this attribute is undesirable, if it is
perceived as having been achieved, the value is desirable, otherwise
the attribute is not bound. The value for this attribute is derived from the
agent's construal of the eliciting situation.
- Pleased.
This attribute represents the valence of the self agent's
response to the emotions of another agent.
It is used strictly in situations in which two conditions hold: (1) an
eliciting situation perceived as a goal-relevant event ``happens'' to
another agent (bound to the attribute other), and (2) the agent
bound to the attribute self is related to the agent bound to other either by friendship or animosity. Once the value
of other has been determined from the eliciting situation, the value
of desire-other has been determined from a Concerns-of-Other
representation (chapter 2.8), and the relationship (friendship or
animosity) has been determined from working memory, the value of pleased can be determined. For example, if an agent has a friend, and the
agent observes some situation in which he perceives his friend to have
achieved one of his (the friend's) goals, the agent can be pleased about it.
This attribute is derived from the desire-other attribute in
conjunction with working memory.
- Status.
When bound, this attribute represents the status of an expectation. When
a situation perceived as an event takes place, in most cases it has no status attribute associated with it (i.e., the status attribute is not
bound). The question of expectations does not arise - an event just happens
and that is the end of it. On occasion, however, events may lead to
expectations on the part of agents. They may also confirm or
disconfirm prior expectations. The representation of such expectations is
problematic and is treated in depth in section 3.3.
Briefly, a situation perceived as an event may be unconfirmed, meaning
that the self agent perceives it as possible, but as not yet
actually having taken place. Once an agent has had this perception, a schema
representing the expected outcome is stored in the
agent's expectation database. At this point, if some situation matching this
schema comes about, then the situation will be interpreted as having been
confirmed or disconfirmed depending on the nature of the
confirming situation. Some of the values for this attribute are taken from
the situation; some are derived from prior expectations.
- Evaluation.
This attribute represents the self agent's assessment of the
eliciting situation as containing a praiseworthy or blameworthy act. If the
agent perceives that such an act has been performed by some (not necessarily
other) agent then he may evaluate this act as in accordance with, or perhaps
in conflict with, his principles. If it is in accordance with his principles
the evaluation attribute is bound to praiseworthy. If it is
in violation of his principles the attribute is bound to blameworthy. Otherwise, the attribute is not bound. The value for this
attribute is derived from the agent's construal of the eliciting situation.
- Responsible Agent.
This attribute represents the agent that the self agent holds
responsible for a perceived praiseworthy or blameworthy act. If it is bound,
it is always bound to some agent's name. It may be bound to the same name as
the self attribute. The value for this attribute is taken from the
agent's construal of the situation.
- Appealingness.
This attribute represents the self agent's assessment of the
eliciting situation as containing an attractive or repulsive object. If
this attribute is bound its value is either appealing or unappealing, according to the agent's tastes. The value for this
attribute is taken from the agent's construal of the situation. Note that
the object itself is not relevant to the generation of emotions and so is
passed in the bindings list rather than in the emotion eliciting condition
relation.
We now present two simple examples. In the first we suppose that Tom has the
goal of making it through the day without getting a speeding ticket. If he
achieves his goal then this situation is construed by him as
desirable, and if he fails to achieve the goal it will be construed
as undesirable. (We assume the simple case where has no
expectations one way or the other.) Let us suppose that the latter case
obtains, and that Tom is stopped by a policeman and given a traffic
ticket. The EEC relation derived from construing this as an undesirable
situation would have ?self bound to Tom and
?desire-self bound to undesirable, and is shown in
figure 2.5. This configuration of
features is necessary, and in this case sufficient, to meet the eliciting
conditions for a distress emotion (see section 2.1). The features for which no value is shown
may be thought of as bound to the value none, although this is not
strictly necessary. In general, each different interpretation of an
eliciting situation will produce a different EEC relation. Thus, for
example, interpretations of an eliciting situation regarding the
attractiveness of an object, which involve determination of the
appeal attribute, will not be mixed with interpretations of the
situation as containing an praiseworthy or blameworthy act, which involves
determination of the evaluation and responsible-agent
attributes. This is discussed later in more detail in the sections on
compound and multiple emotions (sections 2.4.1 and 2.4.4).
Figure 2.5:
An Emotion Eliciting Condition relation for distress.
| self |
other |
desire-self |
desire-other |
pleasingness |
status |
evaluation |
responsible agent |
apealingness |
| Tom |
|
u |
|
|
|
|
|
|
In the second example, Tom again gets a speeding ticket.
This time however, he has a friend, Harry, who observes the
situation. It is Harry's point of view with which we are concerned:
he is displeased about the bad fortune of his friend Tom,
and feels sorry for him. To represent this second point of view, we need a
second Emotion Eliciting Condition relation. Such a relation is shown in
figure 2.6 In this case ?self is bound to Harry,
?other is bound to Tom, ?desire-other is bound to undesirable, and ?pleasingness is bound to displeased. This
configuration is necessary (but not sufficient - see
section 2.4.3) to meet the eliciting conditions for pity.
Figure 2.6:
An Emotion Eliciting Condition relation for pity.
| self |
other |
desire-self |
desire-other |
pleasingness |
status |
evaluation |
responsible agent |
apealingness |
| Harry |
Tom |
|
u |
d |
|
|
|
|
Note that it is not necessary or even likely that Harry has goals
with respect to Tom not getting a ticket. He is only interested in his
friend Tom's general welfare. Probably this eliciting situation is not of
direct interest to Harry at all, although it is possible that he has, for
example, made a bet about Tom getting speeding tickets.In any case Harry's own direct personal goals can be seen as
distinct from the (possibly conflicting) goals he may have with regard to
Tom's fortunes.
Many goals, standards and preferences may be specified in a
hierarchy so that features may be inherited. The interpretation schema of a
getting-speeding-ticket situation might be represented as a set of
goals. In this approach the situation itself is considered to block a
low-level goal, getting no speeding tickets. This in turn may be a
subgoal of a retain money goal which may be a subgoal of,
respectively, retain resources, increase profits, be wealthy and be secure, which is a high-level goal. Inherited features might be that
money is quantifiable, that amount of money lost/found is
important in calculating thresholds, that money goals can have both
positive and negative outcomes, and so forth. The concepts represented in
these slots, both local and inherited, are used to interpret an eliciting
situation and to reduce it to the nine attributes of the Emotion Eliciting
Condition relation.
One final point is important. The number of EEC relations and the
number of different emotion instances following an eliciting situation can
both vary. Sometimes one eliciting situation can generate more than one
EEC relation, even for the same agent, and sometimes one EEC relation can
generate more than one emotion. These issues are discussed in depth in the
following section.
2.4 Generating emotions
Once a set of EEC relations has been generated for an agent,
the relations are used for generating emotions. This
section discusses how this is done within the context of the underlying
cognitive theory. There are a number of complex issues. The first of
these has to do with the generation of mixed and multiple emotions. Since
the discussion is lengthy it will have a section devoted to it. Following
this we show how reasoning about multiple emotions, and other
complicating issues, are dealt with to produce actual emotion instances from
the set of eliciting conditions.
Compound emotions are generated when an agent is seen as being
accountable for
some blameworthy or praiseworthy act that has like-valenced consequences
with respect to the goals of some, not necessarily different, agent. The
four possible compound emotions are shown in table 2.7.
Figure 2.7:
Compound Emotions
 |
As an example, consider again the eliciting situation where
Tom gets a speeding ticket. Suppose that Tom blames the policeman
for giving him a ticket by invoking a principle that says, in effect, that
Policemen should not stop motorists for speeding when they are
traveling at the same speed as everyone else. Taken separately, the
blocking of the get-no-speeding-tickets goal leads to distress, as
discussed above, and the violation of the principle leads to reproach. Together, however, this combination represents the eliciting
conditions for anger, and this emotion replaces the other two.
In the Affective Reasoner, a construal leading to a compound emotion
is represented as a single compound EEC relation derived from combining two
individual EEC relations into one. In the speeding ticket example, this
would mean replacing the EEC relation derived from the construal of a goal
being blocked and the EEC relation derived from the construal of a standard
being violated with a single EEC relation representing both. This EEC
relation is shown in figure 2.8.
Figure 2.8:
An Emotion Eliciting Condition relation for anger.
 |
2.4.2 Subsumption of constituent emotions
Does anger really subsume distress? Do compound emotions always
subsume their constituent emotions? That is, in feeling anger does a person
also feel distress and reproach? This is a difficult question.
Unfortunately, since we are implementing a platform that generates discrete
instances of emotions, we cannot finesse this issue. Either they do or they
do not. There can be no middle ground until the eliciting condition theory
is extended, and the EEC relations extended. For our purposes here we have
made the arbitrary decision to replace the constituent emotions with
the compound emotion.
There are some technical details to be considered with respect to
this issue. Previously we stated that the EEC relation shown in
figure 2.4 was necessary, but not sufficient to meet the
conditions for distress. All of the attributes necessary
for distress (i.e., ?self and ?desirability) are bound,
but if, in addition, the attributes for ?evaluation and ?responsible-agent are bound then the generation of distress may be
blocked. For this reason, the presence of the first two attributes is not
enough to guarantee the generation of the distress emotion.
Similarly, the presence of bindings for ?self evaluation and ?responsible-agent is necessary for the generation of the standards-based
emotions, but not sufficient since the elicitation of
these emotions may be blocked by the presence of a
binding for ?desire. In general, necessary conditions are those
specified above, sufficient conditions further require that ?evaluation
not be bound to a similarly-valenced value for the goal-based emotions, and
that ?desire not be bound to a similarly-valenced value for the
attribution-based emotions.
Except for these two cases the bindings for the remaining attributes
are ignored. For example, a binding for appealingness in an EEC
relation has no bearing on the generation of either the goal-based or
attribution-based emotions, or the compound emotions. The EEC relations in
figure 2.4 and figure 2.5 have, respectively,
seven of nine and five of nine attributes with no bindings shown. In most
cases these attributes will, in fact, have no bindings. This is because
construal frames tend to calculate and bind only those values necessary for
the particular type of interpretation they represent. For example, a
construal frame for attraction interpretations is not likely to create
bindings for responsible-agent, and a construal frame for
fortunes-of-others interpretations is not likely to have bindings for desire-self. However, sometimes bindings for these unrelated attributes do
show up in an EEC relation (a common unrelated attribute is the one for ?other). When they do they are simply ignored.
2.4.3 Multiple emotions
An agent may have many concerns relevant to a single type of
situation. Situations seen as events may facilitate or interfere with
several goals at once or may achieve one goal while interfering with
another. The situations perceived as containing a praiseworthy or
blameworthy act may uphold or violate several principles at once, or may
both uphold and violate different principles at the same time.
Situations seen as containing objects may both attract and repulse at the
same time for different reasons. These different, effectively simultaneous,
construals lead to multiple emotions.
There are no restrictions placed on which construal frames may be
placed in an agent's GSPs. Any construal frame may coexist with any other
construal frame. This means that for any given situation EEC relations may
be produced which lead not only to multiple emotions, but to conflicting emotions. We see this not as a weakness, but rather as a
requirement of emotion reasoning. It is, after all, consistent with the way
humans perceive the world. We have all had situations which lead to
happiness on our parts, which nonetheless have a bittersweet quality to them
because there are aspects of sadness as well. Most of us will also have had
the experience of happiness over the achievement of some goal, while also
feeling ashamed of the way in which we achieved it. These are
examples of conflicting emotions, and a system that reasons about emotions
needs to be able to represent them.
A special class of multiple emotions is those where an agent has the
same emotion, but for different reasons. An agent might be happy, for
example, when he wins at a game of cards. On the one hand he might be happy
because he is competitive and simply likes to win when he competes. On the
other hand he might have made a bet on the outcome and have achieved a money
goal as well. The generation of these emotions is straightforward, but an
associated activity, that of explaining the emotion is more difficult.
Why was the agent happy? Is
he happy twice for two different reasons, or is he just twice as happy for a
combination of reasons? This may seem an esoteric point, but from an
implementation standpoint it has to be resolved,
especially with respect to the
following discussion on the generation of multiple instances of compound
emotions. In the Affective Reasoner, we have simply generated two
instances of the same emotion in response to a single eliciting
situation.
The general problem is that the emotion types are treated as
discrete, non-overlapping categories. An agent is either in one state,
another state, or both. This affects the explanation of emotion instances.
For example, suppose that one of the concerns of some agent, Tom, is
that he has a job he likes and another is that he has a job that pays a good
wage. A prospective employer calls him and offers him a well-paying job with
interesting work. Clearly Tom will be happy about this. In the
Affective Reasoner paradigm Tom will be happy because he now has a
well-paying job, and he will be happy because he is going to have
interesting work.
Now suppose instead that Tom gets a job that pays very well
indeed, but that the work is not very interesting. In this case he might have
mixed emotions: happy that he will
be getting a good salary, but sad that he must now do boring work. In
the Affective Reasoner paradigm this situation is handled by generating
two distinct emotions for Tom.
So far, the approach we have taken (i.e., generating distinct
emotion instances for each EEC relation) is reasonably consistent with the
sophistication level of the rest of the system. The approach breaks down
however when we come to explanations of action generation for the emotions.
Clearly when one is manifesting emotions in response to a situation, then
those manifestations should represent an integrated response to the
situation and to the dominant emotion.
It would be ridiculous, for example, to have an
explanation that said, in effect, that Tom was smiling
because he got a well paying job, but was laughing because he got an
interesting job. Certainly Tom is both laughing and smiling simply because he is happy, and he is happy both because he got an
interesting job and it pays well.
We address this problem superficially in the conflict resolution
component of the action generation module (see chapter 4).
The basic idea of this component is to remove actions which are not
compatible with one another. While this helps to avoid outlandish behaviors
on the part of the system, it does not address the integration issue at all.
One further point is that the makeup of the manifestation of mixed
emotions is presumably dependent upon the intensity of those emotions. One
emotion may take precedence over another if it is much stronger. In
addition, conflicting emotions may, in some cases, tend to counteract the
manifestation of each of those emotions. For example, someone who is both
happy and fearful at the same time (e.g., when finally getting a chance to
bat in the big leagues) may have an intense bodily response
expressing both emotions simultaneously. He is unlikely, however, to cower
out of fear, because he is also happy; he is unlikely to simply relax in
bliss because he is also afraid. Except for the superficial treatment of
conflicting expressions of emotions, the development and implementation of a
theory of the expression of multiple emotions is beyond the scope of this
work.
2.4.5 Multiple compound emotions
Lastly we must consider the problem of multiple
compound emotions. Multiple emotions of the same
type can lead to problems when they are subsumed by compound emotions.
Suppose that the agent, Tom, who is looking for a job, has three
goals: (1) he wants a well paying job, (2) he wants an
interesting job, and (3) he wants a job close to home. In addition he has
confided in a friend that he has a felony conviction in his past that he did
not mention to a prospective employer who is offering just such a job. He
has two standards associated with this confidence: (1) a person should never
betray a friend, and (2) a person should not ``tell'' a secret. The
prospective employer calls the friend for a reference and the friend does
not mention the prior felony conviction. Tom gets the job.
Figure 2.9 shows the possible explanations for Tom's
gratitude.
Figure 2.9:
Explanations for Tom's gratitude.
| The d X a approach |
| not telling secret helped get a good paying
job |
| not telling secret helped get an interesting job
|
| not telling secret helped get a job close to home
|
| not betraying friend helped get a good paying job
|
| not betraying friend helped get an interesting
job |
| not betraying friend helped get a job close to home
|
These explanations for the source of Tom's gratitude
illustrate the 
approach where 
represents the desire-based
construals and represents the attribution-based construals. This
approach is not
very good. While each of the explanations might itself be valid (i.e., such
eliciting conditions might well lead to gratitude), none of them seems
to be what Tom is likely to think of as the cause of his emotions.
Still it seems possible that Tom is grateful to his friend for several
different reasons.
A second approach, the 
method, is to group the desire-based
construals together and the attribution-based construals together for
different explanations, as shown in figure 2.10. The
first three explanations represent Tom having gratitude because he construes
his friend's actions (all of them taken as a group) as ``something
admirable'' which he assess in the light of three of his goals. The fourth
and fifth explanations represent Tom having gratitude because he construes
his friend's act of not telling a secret as helping to achieve his goals
(all of them - with respect to this situation - taken as a group) and
construes his friend's act of ``not betraying him'' similarly helping him to
achieve each of his three goals. One can imagine someone thinking: ``I am
grateful because he did some things which were admirable and that helped me
get a well-paying job, it helped me get an interesting job and it helped me
get a job close to home'', and ``I am grateful because he was trustworthy
with respect to my secret, and that helped me get some things I desired. I
am also grateful because he was a good friend to me, and that also helped me
get those things I desired.'' This approach seems palatable and yet still
seems to contain more power as an explanation than, ``I am grateful because
he did some admirable things that helped me get some things I wanted.'' For
this reason, it is the approach we have adopted in the Affective Reasoner.
Figure 2.10:
Grouped explanations for Tom's gratitude.
 |
Obviously this is a matter open to debate: at what point do humans
cease to discriminate individual goals and standards and
start to
group them together? Fortunately, given the coarse-grained nature of the
emotion reasoning performed by the Affective Reasoner, this turns out to be
not particularly critical to the working of the system. Such multiple
compound emotions rarely arise in practice. This issue is important
theoretically, however, because it highlights the problems one encounters
when taking such a rigidly discrete approach to the classification of
emotions and of emotion instances.
The Emotion Eliciting Condition Theory based on [18] is
represented in a separate database as a set of high-level emotion rules. One
result of this design choice is that all emotion-based processing
must, at some point, pass through these rules. In other words, to get from
an eliciting situation to the creation of some new simulation event based on
the emotional response to the eliciting situation, at least one of the
high-level emotion rules must be used.
This has two benefits. First, it helps us to
assess the underlying eliciting condition theory: since all emotion
processing is based, at least partially, on the rules representing
the theory, if the theory is lacking we will soon know it, when
we try to represent those situations where its representational power
fails. Another way to think of this is as follows: if the system
is successful in its attempt to realistically represent a rudimentary
emotion system,
then this tends to verify the underlying
theory. If the system is unable to successfully represent certain aspects of
such a rudimentary emotion system, then we may be able to trace its failings
to a failing in the underlying theory.
Second, if the emotion eliciting condition theory has to be changed
then only a simple modification to this strictly formatted rule-base need be
made to change the system as well. There is exactly one of these high-level
rules for each of the twenty-four emotion classes, so changes to the emotion
eliciting condition theory for one of the emotion classes would mean,
literally, changes to only one rule.
Each of the twenty-four rules has a left-hand side which can
be matched by certain configurations of EEC relations, and a right-hand side
containing an emotion template. When the left-hand side is
instantiated by some instance of an EEC relation then an emotion template is
bound using the bindings created during the construal process. This
emotion template represents an emotional state on the part of the agent
interpreting the situation and is the output from the construal phase.
The emotion template has slots for (1) the name of the agent having
the emotion, (2) the name of the emotion type, (3) the initiating situation
ID number, (4) the bindings created during the construal process, and (5)
the additional identification of some agent, in the case of both the
attribution emotions and the Fortunes-of-Others emotions.
Figure 2.11 shows an instantiated emotion
template for anger:
Figure 2.11:
An instantiated emotion template for anger.
 |
Once an emotion template is generated in response to an
eliciting situation, the construal phase of the system has completed its
processing. The next section reviews the steps we have illustrated so far.
We have described how eliciting situations may arise from the
simulation events. These situations may or may not be of concern to one or
more agents. If they are, then varied interpretations of the situation may
be made, depending upon the makeup of each agent's interpretive
personality component. These interpretations are reduced to Emotion
Eliciting Condition relations, which in turn are used to generate
instantiated emotion templates.
Figure 2.5 illustrates the different sources for these
emotion templates in the Affective Reasoner, and serves as a summary for
this part of the chapter. We now present a brief description of the steps in
the diagram.
Figure 2.12:
Structure of the mapping from a situation to emotions
 |
- 1. A situation is created when a simulation event is
popped off the queue and the state of the simulated world is
altered in a way that might be of concern to one or more agents.
- 2, 3. The frames representing the goals, standards and
preferences (GSPs) of some agent are matched against the eliciting situation
frame. Working memory is attached to slots in the GSPs and can alter the
success of the match, as well as the resultant bindings that are generated.
- 4, 5. When a match succeeds for some construal frame (and
its inherited properties) then the situation is considered to be
relevant to the agent's concerns. Since an agent may simultaneously have many
different construals of the same situation, multiple interpretations
of that situation may result. When all matches fail, the situation
is not considered relevant to the agent's concerns
and is ignored.
- 6. If the situation is relevant to the concerns of
the agent, bindings
will have been created during the match process. In addition to those bindings
created when slots in the situation frame are unified with pattern matching
variables in slots of the construal frame (including inherited slots),
additional bindings may come from attached procedures and from working
memory.
- 7. A basic Emotion Eliciting Condition Relation (EECR) is
created for each construal of the situation.
- 8, 9. The status attribute of the EECR may be changed to
confirmed or disconfirmed if the situation is relevant to
a stored expected outcome.
- 10, 11. Since there may be multiple construals there may be
multiple EECRs. These are collected together before further processing.
- 12, 13. The event-based construals and attribution-based construals
are split off into two (non-intersecting) sets. They are recombined, as
applicable, to form the compound-emotions EECRs, thus subsuming the
component EECRs. When recombination is not applicable (e.g., in the
case of a blameworthy act helping the agent to achieve a goal) the original
EECRs are passed along instead.
- 14. The domain-independent rules contain the backbone of the
emotion eliciting condition theory. All emotions are generated using
these rules. When an EECR matches the left hand side of one of
these rules, an emotion instance is generated.
- 15, 16, 17, 18. Those emotions not about the fortunes of others are
the direct emotions. A subclass of these is the prospect-based
emotions of hope and fear. These latter emotions stem from
unconfirmed events and so generate expectation frames.
These frames
are stored, and may be used to interpret future situations.
- 19, 20, 21. For each other agent involved in the situation,
the target agent's representations of their concerns (i.e., the
Concerns-of-Other databases the target agent maintains for them)
are used to interpret the situation, similar to the process in
steps 2, 3 and 4.
- 22, 23, 24. If the situation is determined (partly by
assumption) to be of concern to the other agent, then the domain-independent
rules are used to determine the meaning for the target agent. Is he pleased
about the outcome for the other agent? Relationships between the agents are
stored in working memory and may be altered as the simulation progresses.
- 25, 26, 27, 28. Each of the resulting fortunes-of-other EECRs
is used to produce an emotion with the domain-independent rules.
- 29, 30. The direct and fortunes-of-others
emotions are all collected into a group and passed to the action
generation module. Included in each emotion instance is set of
bindings from both the original match and the intermediate processing.
This topic, Action generation, is covered in detail in
chapter 4. We discuss it briefly here
to place it in larger context
of the system. Many questions about the theoretical reasons for
implementation decisions are raised, but are not addressed here since they
will be covered in the the later chapter.
In this section we use the terms temperament trait, response
action, and emotion manifestation, all of which need
clarification. Borrowing from Reber [20], we define temperament traits to be the enduring characteristics of an agent that can
serve an explanatory role in accounting for the observed regularities and
consistencies in an agent's behavior with respect to the manifestation of
emotions. The temperament traits may be thought of as descriptive
theoretical entities, whereas their representation, the structures making up
the response action categories, may be thought of as the
prescriptive mechanism used for generating such manifestations.
Once an emotion state has been established for some agent,
processing of his action response (i.e., the manifestation of his emotion) is
initiated. Responses are divided into about twenty response categories,
described later in this section. These categories are
based on the work of Gilboa and Ortony [13]. The categories are
descriptive in nature, are intended to cover the entire set of possible
responses, irrespective of the nature of the eliciting situation. While
the response categories are often the same for each of the emotions (e.g.,
both anger and joy have communicative verbal response categories in
which they can be manifested), the actions associated with each
category are emotion-specific and, of course, must be relevant to the object
domain. The actions may be simple tokens (such as 
laugh
)
which are not further constrained by the eliciting situation, they may be
templates, such as laugh at ?other-agent, or they may be
mini-plans such as (the plan for) get-revenge. The latter two
are dependent upon the eliciting situation and the construal process for
binding values. Our current research has not emphasized the mini-plan form.
Response actions may themselves be simulation events and may thus initiate
and modulate further processing.
The Affective Reasoner's emotion manifestation lexicon may be
viewed as a three-dimensional array whose cells are filled with response
actions. The first dimension of the array is the twenty-four emotion
classes. The second dimension is the approximately
twenty emotion manifestation
categories. The third dimension represents a partial intensity ordering
of the several response actions for each of the parent cells.
Theoretically, the response-action dimension may be further broken
down into two tiers, creating a hierarchy: non-goal directed
responses (including expressive and information processing responses), and
goal directed responses (including affect-oriented emotion regulation and
modulation, and plan-oriented responses). Since there are no inherited
properties from these additional levels, such a theoretical division does
not affect processing. Future work would, however, make use of this
further breakdown of the domain.
2.6.1 Action Response Categories
Each of the categories that comprise the second dimension of the
emotion manifestation space discussed above may be enabled or disabled for a
given agent at a given point in the simulation run. If a category is
enabled, then actions in that category, for a specific emotion, may be
selected as manifestations of that agent's emotions. Taken together the
particular pattern of enabled and disabled response categories gives agents
their unique manifestative personality components. Consequently, the
individual categories represent the potential temperament traits of an
agent. Lastly, these temperament traits are usually enabled or disabled for
all of the positive emotions at once, and for all of the negative emotions
at once. The categories we employ, based on [13] are as follows:
- Somatic responses. Bodily manifestations such as trembling, going
into shock, feeling overall pleasure.
- Behavioral responses directed towards an inanimate object.
Responding to the
environment with attention or action focused on an inanimate object:
slamming a door, kicking something.
- Behavioral responses directed towards an animate object.
Responding to the environment with attention focused on an animate object
such as hitting someone, pushing someone, etc.
- Behavioral responses that are unfocused. Responding
through actions without attending to another agent or object, e.g., jumping
up and down, and sitting quietly.
- Communicative non-verbal responses. Intentionally
revealing one's emotional state through non-verbal actions such as smiling,
winking, throwing arms up in the air, and shaking one's fist.
- Communicative verbal. Intentionally revealing one's emotional
state through words: ``rubbing it in'', telling others, etc.
- Evaluative self-directed attributions. Ascribing qualities
pertaining to one's social status, inherent worth, or moral standing,
qualities such as superiority, of invincibility, stupidity,
and so on.
- Evaluative agent directed attributions. Ascribing qualities
pertaining to some other agent's social status, power, inherent worth or
moral standing.
- Obsessive attentional focus. Excluding other contexts, concepts
or agents so as to continually monitor or evaluate a some aspect of the
emotion or emotion-inducing situation. For example, focusing exclusively on
the other agent, a blocked goal, future consequences, and so on.
- Repression. Excluding unacceptable desires and impulses
from consciousness. For example, denying positive valence.
- Suppression. Conscious intentional attempts to exclude
thoughts or feelings, such as laughing when sad, showing compassion when
feeling gloating, etc.
- Reciprocal. Causing the other responsible agent to feel
a similarly valenced emotion if not a similar emotion. Examples
include threatening and thanking.
- Reappraise self. Changing one's assessment of his
role, status or inherent value. For example, assessment of
one's self as a winner, as a loser, or as more or less powerful.
- Reappraise situation. Changing one's assessment of
the situation so as to see it as modifiable or insignificant.
- Other-directed emotion modulation. Changing another agent's
emotional state by, for example, inducing embarrassment, or threatening him.
- Situated plan initiation. Responding to the event with an
immediate logical response such as running away, ducking, or telling of
love.
- Full plan initiation. Ruminating to construct or select a
complex response and then initiating it. A classic example here is planning
revenge.
Figure 2.16 shows an overview of the action generation
component of the Affective Reasoner. What follows is annotation for the
various elements in the illustration:
Figure 2.13:
The structure of action response generation
 |
- 1, 2, 3, 4, 5. An eliciting situation arises. This is passed
directly to the modules that build Concerns-of-Others representations,
as discussed in section 2.7 below. In addition,
features are extracted and passed, along with features of the action
response, to the feature reader for the observation component of
the Affective Reasoner, also discussed in section 2.7.
- 6, 7. As discussed in section 2.4,
emotions are generated in response to eliciting situations using the interpretive personality components of agents. These emotion instances are
passed, along with the bindings created during the construal process, to the
action generation module.
- 8. The emotion instance activates a portion of the action
database representing that particular emotion, for that particular agent.
- 9, 10, 11. Depending upon the composition of manifestative
personality component for the agent, certain temperament traits
will be enabled.
- 12, 13. (Usually) one action is selected for each enabled
temperament trait, for the selected emotion, using the bindings generated
from the construal process. Together these selected actions comprise the set
of candidate actions.
- 14, 15. The set of candidate actions may contain incompatible
manifestations (e.g., jumping up and down and quietly
reflecting). A database of conflict sets is used to identify
and resolve such conflicts. Incompatible manifestations are removed
using a pseudo-random selection algorithm.
- 16, 17. The response actions (emotion manifestations)
are fed back into the simulation queue to possibly modulate future
processing. These response actions are also combined with selected
features from the eliciting situation and passed to the feature
reader for the observation component.
2.7 Observing actions
Because this area of the Affective Reasoner is not fully developed,
it will be treated briefly, and only in this section. The observation
mechanism described here is closely tied to building Concerns-of-Others
structures, a topic covered in the next section. Indeed, much of the
motivation for observing the actions of other agents is to decide what
their concerns are, for future reference.
Within the context of this section, we define an emotion
episode as an emotion eliciting situation taken together with an
intermediate emotion and any resultant action responses on the part of the
participating agents. For an observing agent the episode has two constituent
parts: that which the observer can ``see'', meaning the features of the
eliciting situation together with the features of the agent's emotion
manifestation, and that which the observing agent cannot ``see'', meaning the
construal of the eliciting situation which lead to some emotion. It is the
abduction of the second of these constituent parts which is the subject of
this section.
The problem of how to observe emotion episodes and make plausible
assessments of what the intermediate emotions were is a classification
problem. In other words, the question we are asking is: what emotion
makes this episode make sense?
An emotion episode is classified by recognizing that its attributes
imply that it is an instance of a particular emotion class more strongly
than they imply that it is an instance of any contrasting emotion class. To
classify reliably, agents in the Affective Reasoner must have an accurate
representation of the correspondences between attributes and concepts (i.e.,
between eliciting situation features and emotion-based action responses of
some agent, and the emotion classes).
Since our agents are dynamic entities, with varied rudimentary
personalities and varied moods, we need a dynamic mechanism for
understanding their action responses to eliciting situations. Agents are
also very complex: the number of different responses to eliciting situations
is large, even in such a limited system as the present implementation.
Because of this, to store a useful static representation of a classification
system would be prohibitive.
This suggests that our classification mechanism should adapt to
current needs of the agents in the system, giving less priority to knowledge
that is not used often, and a greater priority to knowledge that is. The
mechanism should learn only that small portion of the structure of the emotion
manifestation domain that is necessary to do its task in a specific set of
circumstances, without being encumbered with knowledge it will not use. To
meet these needs, and to perform the sort of flexible reasoning required in
any weak-theory domain, we chose to use Protos, an exemplar-based
knowledge acquisition program developed by Ray Bareiss [2].
To quote:
-
Protos learns as a byproduct of performing classification under
the guidance of a human teacher. When presented with the description of an
entity (i.e, a case) to be classified, Protos attempts to recall a
previous case and to explain its similarity to the new case. When it cannot
correctly classify or adequately explain a case, Protos interacts with the
teacher to obtain the correct classification and an explanation of why it
is correct. It learns by selectively retaining cases and their explanations.
[2] (page 4)
Protos interacts with the user as the simulation proceeds. It is
through this interaction, with the user taking the role of the ``emotion
manifestation domain expert'', that Protos acquires its knowledge about
emotion manifestations as they arise in the context of the simulation. The
sum of the interaction between the user and Protos is the sum of
transference of domain knowledge from teacher to automated student.
Protos' job, in this application, is to give observing agents the
ability to make judgments about what emotion is taking place in
any given episode. Within the closed process of situation-emotion-action,
input is a structure containing features of the
situation and features of the emotion manifestation (i.e., the action responses of
the agent having the emotion). Output is an assessment of which emotion took
place. Additionally, depending on the mode of operation, other input and
output may arise which is external to the simulation, as Protos asks for
help in classifying cases and assessing the role of features in those cases,
and as Protos offers justification for its classification of a new case.
The purpose of the observation and classification mechanism is to
permit an observing agent to conjecture what emotion some other agent is
experiencing. This information is then used as leverage for explaining why the other agent acted a certain way in response to a situation.
For example, when Harry sees Tom swearing and shaking his fist after getting
a speeding ticket then Harry may look at past cases and discover that other
times when someone was swearing and shaking their fist then they were angry.
Once Harry makes the assumption that Tom is angry he can ask the question,
Why is Tom angry? which in turn leads him to reason about the
Tom's interpretive personality component.
Features of a situation and features of an agent's responses to those
situations are observed by some other agent. These features are then
compared with past cases to see how to classify the episode. Is it a case of
pride? Is it a case of fear? Once a classification has
been made, and verified through one of the methods given below, the
information used to classify the case is also used to store it for
future use. Parts of the new case which are not understood have to
be explained to the system by the teacher.
Figure 2.14, for example, shows how a stored exemplar of
anger can be adapted to match a new case. Here the observing agent has
stored two previous cases of anger. In the first case an angry agent has
been observed shouting, shaking his fist, turning red, and scowling. In the second case an angry agent has been observed swearing, shaking his fist and trembling. Furthermore, the
observing agent knows that both shouting and swearing are communicative, expressive, non-goal oriented emotion manifestations for
anger. He also knows that shaking fist is highly indicative of anger.
A new case is encountered, containing the features, swearing, shaking
fist and turning red. Using the reminding of shaking fist the
agent retrieves the two previous cases. Substituting swear for shout in exemplar one (since they have the same expressive function for
anger), a match can be created for classifying the case.
Figure 2.14:
Using a case to retrieve anger
 |
Figure 2.15 shows how feature links can be used as censors
to represent negative associations between features and categories. In
this example swearing shows up as a feature in past cases of
both anger and joy (from time to time agents may swear as an expression
of joy). This is a stronger reminding of anger than it is of joy, but
the second feature, laughing is not compatible with anger and
so causes Protos to choose the first exemplar as the closest match.
Figure 2.15:
Remindings and censors
 |
When a classification has been made and verified, some features
may be unaccounted for. In this case Protos asks the teacher for
help. The unaccounted for features are discussed in the light of
the present case. Protos has thirty-one predefined relational links,
and ten qualifiers. These are used to link features in a case to
the classification category. Since Protos is a general purpose
representational system a number of relational links, such as
acts on are not useful for emotion manifestation classification.
Others, such as suggests and is mutually exclusive with
are more appropriate.
Observation can take place in one of two different modes.
In the first mode the observing agent is simply told by the system
what emotions are present in the other agent, as though he were ``asking''
the observed agent himself, and as though the observed agent always knew the
correct answer (certainly not the case with humans). This information is
then used for the construction of Concerns-of-Others databases, discussed
below in section 2.8. Protos is not used in this mode.
In the second mode the agent uses Protos to attempt to determine
what emotion is present, reasoning from past cases about the features
present in the eliciting situation and the other agent's response actions.
Feedback is given by the ``teacher'' (in this case the user) and includes
both assessment of Protos's emotions classification (i.e., was the
classification correct?) and clues about the functional, causal,
correlational, mutual exclusion, etc. relational links from observed
features to the emotion category as well. For example, using Protos to
operate on past cases, an observing agent may decide that another agent who
is shaking his fist in the air is expressing anger. Protos first wants to
know, Is this correct, that the agent is experiencing anger? Next
Protos wants to know, if it does not already know from previous cases, what is the relationship between shaking one's fist and anger? Lastly,
Protos wants to know, how predictive is this feature of anger? Any new
emotion manifestation domain knowledge that Protos has learned in this
interaction with the teacher is then stored as part of the current case.
2.7.4 Organizing the emotion manifestation domain as cases
What is a workable, expressive lexicon for emotion manifestation
within a limited domain? How can we use the representational power of
Protos to classify and store old cases so that they can be used effectively
for understanding new cases? To answer these questions fully, we would need
a broad content theory of the manifestations of emotions. In addition we
would need to capture that content theory in a structural representation of
each object domain. (For example, what does it mean when one taxi driver
``cuts off'' another driver? In what way might this express anger?)
In other words, we must first decide what the relational links are
from the attributes of the emotion manifestation domain back to the emotion
categories. What is the relationship, for example, between the non-goal-directed, expressive, behavioral-toward-animate emotion
manifestation category (see section 2.6.1) and anger?
Second, we must then decide upon links from constituent tokens
in these categories (i.e., the observable manifestations). For example, what
is the relationship between the scowling attribute and anger? Lastly
our content theory must include features from the object domain. For
example, what is the relationship between a taxi driver scowling at
another driver and anger? We do not propose such a content theory here.
However, we do suggest a start on a theory that has, at least, given us
minimal functionality in this component of the Affective Reasoner.
As will be discussed in section 4.2 we track
approximately 1440 different actions for the twenty-four different emotion
categories, about sixty apiece. There are obviously many different cases
that can be generated from different combinations of these actions. To fully
explore the ideas presented here one must then develop a very large library
of cases. This, at present, is beyond the scope of this work. What we do
present, however, is
a six step account of how we have used Protos to classify such cases in a
useful way.
Step one. As discussed previously in
section 2.6.1, action responses to emotions have been divided
into about twenty categories. These categories are purely descriptive in
nature but each may be thought of as being associated with some biological
or cognitive function. For example, somatic responses may well serve
the purpose of preparing the body for some sort of action or trauma, and
expressive responses may serve the purpose of communicating with
others so that they can help to meet the needs of the expressing agent. We
may use this in building up our structure for organizing emotion
manifestations. As discussed in section 4.4 we
have generally limited emotion manifestations to single actions within one
of the action response categories. We then make a functional
relational link from the emotion to the action response category in Protos,
applicable to each action response token in that category. For example, shouting may serve as a verbal expressive manifestation of anger. In
other words, we are viewing shouting as serving some verbal
expressive ``need'' of the agent which has arisen as a result of his anger.
Using such an organizational scheme gives us some return in
increased expressive power of our cases. Suppose that we have some old
cases, each with a different verbal expressive action response token.
When we attempt to classify a new case we find that it does not match any of
the old cases. However, because we have given some structure to the emotion
manifestation domain based on the supposed functional nature of the
manifestations, we are able to follow links to other cases and substitute
one verbal expressive response token for another, thus creating
a better match.
Step two. The second step in organizing the emotion
manifestation domain is setting up the strengths of remindings from
response actions to emotion categories. This is extremely useful since most
emotions have some prototypical manifestations, some typical
manifestations, some unlikely but possible manifestations, and so
forth. If we observe some agent shouting when the lottery results come
out we may not be sure what he is shouting about; if we observe him shouting and scowling and shaking his fist we may be
relatively confident that he is angry; if we observe him shouting and
smiling we may believe, instead, that he is joyful. In this case shouting is ambiguous, but scowling, shaking fist and smiling
are less so, and help to disambiguate the input.
Step three. The third step is to add mutual exclusion links.
Laughing and anger are generally mutually exclusive, as are crying and reproach. In addition, the mutual exclusion relational link
may be qualified, so we can accurately express the idea that some feature is
usually mutually exclusive with an emotion class, etc.
Step four. The fourth step is to include some causal and
correlational links from features anecdotally present in the eliciting
situation. Getting a speeding ticket may be considered to cause
anger (without specifying why); the presence of Tom, an
agent, may usually co-occur with distress, and so forth.
In the Affective Reasoner this is handled by including features from
the eliciting situation in the structure that holds the action response
features.
Including features from eliciting situations in the case base
provides another way of reasoning from the eliciting situation to the
resulting emotions. This does not take the place of reasoning using the COO
representation of the interpretive personality of the observed agent
however, because, at best, reasoning from observed features will
allow us to use correlations to make predictions about those features and
the resulting emotions. It ignores the strong-theory reasoning that makes
use of the causal nature of the emotion eliciting conditions. In simple
terms, using heuristic classification to identify emotion types based on
features of eliciting situations may tell us what emotion has taken place,
but it will not tell us why. This is discussed further in
section 2.7.4.
Step Five. The fifth step is to run the simulation with
certain personalities (both interpretive and manifestative)
present in the agents. The makeup of these agents then is such that they
each tend to have certain emotions, and they each tend to express them
in a certain way. This allows observing agents to build case-bases from
repeated observations. In Protos, the repeated use of the case library for
classification alters the structure of the library so that certain
prototypical exemplars are given precedence. This means that agents
will form content theories of emotion manifestation tuned to their
particular environments. This prototypicality of exemplars is flexible,
however, and will change if the moods of the agents change so that the
agents either begin to experience a different set of emotions or manifest
the emotions they are already having in a different way.
Step six. Lastly, we may choose to keep
case bases from previous simulation runs. This process, over time, develops
a large set of cases for reasoning about emotions, and tends to de-emphasize
the situation-specific knowledge. This case base corresponds to ``built-in''
knowledge about emotion manifestations and emotion-initiating situations
that may be given to an agent at the start of a simulation, as though that
agent had a long history of emotional interactions with others on which to
draw.
As discussed in section 2.8, consider that
agents in the Affective Reasoner maintain (imperfect) representations of the
concerns of other agents (i.e, copies of what is assumed to be the other
agents' interpretive personalities). This allows them to explain why
other agents respond in the way they do to situations in their world. It
also allows them to predict how another agent may respond to a situation,
which in turn might give rise to a fortunes-of-others emotion on the
part of the observing agent. In simple terms, this allows an agent to
reason as follows: ``I know how Tom will interpret this situation,
therefore I know what emotional state he is in.'' Different agents have
different concerns. Representing the different concerns of the various
agents allows an observing agent to represent them as different individuals
with respect to situations that arise in the simulation.
Absent from this model of other agents is the unique ways
in which the individual agents manifest their emotions. In fact, no explicit
model of this is kept. Such knowledge would be equivalent to knowing that,
Tom shakes his fist when he gets angry, and Harry always wrings
his hands when he gloats. However, since an agent's name is always among
the features of a situation it is still possible to associate an agent with
typical cases of a certain emotion. While it is true that an
observing agent cannot reason
from an explicit representation of some agent's manifestative personality to explain or predict actions, he nonetheless
has some way of associating that agent with certain actions, and
tying those associations into an emotions classification, through the
inclusion of eliciting situation features in his case base.
For example, we may see that Tom often glares when he is
angry. Since we do not have any representation of Tom's manifestative personality component we cannot store the information that
Tom often glares as an expression of anger. This prohibits us from
reasoning backwards from this particular manifestation to the emotion,
except in the general case. On the other hand, we do have knowledge
that says that the features Tom and glaring are often highly
correlated with cases of anger. Should some other agent than Tom be
glaring, we have less confidence that anger is present. In this way we have
linked Tom to cases where he is glaring in anger without having an
explicit representation of this for Tom.
The second point to consider with respect to the storing of
situation features in cases is that this does not take the place of
keeping interpretive personality representations for other
agents. As will be discussed below in section 2.8,
one of the important aspects of these representations is that
they allow an observing agent to explain emotion-based responses
in terms of emotion eliciting conditions and their mapping
to eliciting situations within the object domain. Storing
situation features in cases does not allow for this type of
explanation to be made.
2.8 Personalities of others
In a world of interacting agents it is useful for an agent to keep a
model of other agents with whom he interacts. Emotions are signs of how
those other agents have interpreted the world around them, and therefore
(a) how they are are likely to react to similar situations in the future,
and (b) what sort of agents they are (e.g., laid-back, irritable, etc.).
For an agent to understand how another agent is likely to construe a
situation, he must understand that situation from the other agent's point of
view. Since in the Affective Reasoner frames representing an agent's
potential interpretations of the world are stored in his GSP database it
follows that an observing agent must also have some internal
representation of other agents' GSPs. This knowledge is captured in
knowledge structures known as Concerns-of-Other (COO) databases. They
are, essentially, imperfect copies of other agents' GSPs, and represent
their concerns as modeled by an observer.
Thus, in addition to the GSP database representing an agent's own
concerns, a COO database must also be maintained for each other agent the
original agent is internally modeling. When an eliciting situation arises,
an agent filters it through his GSP database producing an interpretation
of that situation with respect to his own concerns. Using the same process,
the agent may instead filter the eliciting situation through the COO
database for some other agent, producing an interpretation from what the
observing agent perceives as the other agent's point of view.
A perfect COO representation, of course, would be an exact duplicate of the
other agent's GSP database, and would always lead to the same
interpretations that the observed agent would have. However, since, as
discussed above in section 2.2, GSPs are built out of
interpretation modules (i.e., frames), partial COOs can be created
incrementally. Even though they are incomplete, these partial
representations are useful since they allow the agent to interpret at least
selected events correctly. For example, Harry might know that Tom
is a passionate Cubs fan, and that if the Cubs lose he will be upset,
and yet not know more about him. Still, if the Cubs do lose, and
Tom is jumping up and down, then Harry probably knows why.
Since the Affective Reasoner was developed as a general
research platform, several options are available with respect to the
establishment of COO databases. They may be constructed at start-up time as
part of the initial
composition
of agents, or they may be learned as the simulation proceeds and as agents
come into contact with each other. In the former case a number of
difficulties are avoided, such as having to work out the details of when
agents are permitted to observe each other; in the latter
case, many useful knowledge acquisition issues can be studied. For example,
if the purpose of the system is to store knowledge about interesting agents
and study emotions that arise when they interact, then the domain-analysis
investment required for setting up the COO learning process will have little
return. On the other hand, if one is studying user-modeling from an emotion
perspective, such a component could be very useful.
When the Affective Reasoner is set up so that agents learn about one
another's concerns through interactions, COOs are built up incrementally by
locating and incorporating construal frames that seem to explain another
agent's emotional states in response to observed situations. For example,
when Harry sees that Tom is always unhappy when the Cubs lose he might infer
that Tom construes some aspect of this situation as blocking one or more of
his goals. Harry might then try to determine exactly which goals are
involved: is Tom a Cub's fan, or has he just been betting on them to win
lately? Perhaps his brother plays for the team? In the following algorithm,
which describes this process, we assume that an agent has already discovered
the emotion present in the other agent. He now attempts to explain that
emotion in terms of the eliciting situation, and possible construals of that
situation. To do this, the observing agent consults first his COO for the
observed agent (if he has one), and then, if necessary, a global database of
available construal forms (see section 2.8.4). Here is the
algorithm for incrementally building COO representations.
- Locate the Concerns-of-Other representation (COO) for the
other agent. If one does not exist or it does not contain an interpretation
for this type of eliciting situationthen go to 5,
- Filter the event through the COO, producing an emotion. If this
emotion is the same as the emotion that actually was present in the observed
agent then the COO has probably given a correct interpretation of the
eliciting situation so go to 8, else,
- Since the interpretation produced by the COO is incorrect (i.e.,
the emotion based on the COO's interpretation of the eliciting situation
does not match the emotion known to be present in the agent whose concerns
it is supposed to represent) the construal frame used to make the
interpretation should not be part of the concern structure of the agent.
Remove it from the COO,
- Mark the construal frame ineligible for this agent.
Eligible frames are those frames that can produce interpretations for this
type of event. Ineligible frames are previously eligible frames
which have been found to produce incorrect interpretations,
- Search through the global (or default) database
for the next eligible interpretation of this event,
- Evaluate the event using the new interpretation as a filter.
If the resultant emotion is not the same then go to 4,
- Add the construal form to the COO,
- Generate explanation based on the current
construal form.
Once a COO as been established for some other agent it can be used
for two purposes. First, it is now possible for an observing agent to have
emotions based on the fortunes of the second agent. In the Affective
Reasoner this may come about if the agents are in one of the following three
(possibly only unidirectional) relationships: friendship, animosity and
empathetic unit.For example, if the observing agent knows that a second
agent is a Cubs fan, then if they are friends he can feel sorry for
the second agent when the Cubs lose. On the other hand, if they are adversaries then the observing agent can gloat when the Cubs lose.
Lastly, should the bond between the two agents be so strong in some
situation that the observing agent temporarily takes some of the second
agent's concerns on as his own then an empathetic unit has been
formed. The observing agent will temporarily suspend his own GSP database,
using his COO for the second agent to generate direct emotions instead. Note
that even in this case the observing agent might actually be wrong about the
import of a particular situation to the second agent, since it is the
observing agent's representation of the second agent's concerns
which are being used to generate emotions, not the actual GSPs of the second
agent. The second use of COOs is that once they are established it is
possible to explain, and possibly predict, the emotional responses of other
agents based on the eliciting condition rules, as in the previously
discussed case of Tom the Cubs fan.
2.8.3 Satellite COOs
Modeling the simple concerns of other agents, as specified in the
previous section, is still incomplete. Consider the following
story:
- A rookie quarterback is, as usual, sitting on the bench during a football
game. His brother and a woman friend of his brother are in the stands.
Suddenly the starting quarterback goes down with a knee injury. The woman
smiles because she is happy for her friend who's brother will now be placed
in the game.
The following sets of concerns and relationships must be considered for each
of the agents:
- The actual concerns of the rookie quarterback, i.e., his GSPs. Inferred
in the story is that he will be pleased about achieving a getting-to-play goal.
- The supposed concerns of the rookie quarterback as represented
by his brother (i.e., the COO representing the brother's beliefs about
the GSPs of the rookie quarterback).
- The relationship between the rookie quarterback and his brother.
Specifically the friendship relationship, or even
an empathetic unit relationship.
- The friendship relationship between the woman and the brother.
- The supposed concerns of the brother as represented by the woman.
This must include, recursively, her supposed supposed concerns of the
brother for the quarterback as well, and the supposed empathetic relationship
between the brother and the quarterback. In other words, the woman must
have a belief that the brother will believe that the quarterback will
be happy about the starting quarterback's injury. Furthermore, she must
believe that the relationship between the brother and the quarterback
is such that a positive outcome for the quarterback maps to a positive
outcome for the brother.
Since the story gives no clues to the emotional
states in either the quarterback or his brother it should be obvious
that neither the actual concerns of the quarterback nor those of
his brother are necessary for either understanding or generation of
the episode. To make this clear, consider the following possible
continuation to the story:
- ...But the smile quickly faded away when the brother exclaimed, ``Oh no,
I told him he shouldn't have drunk that case of beer at lunch!''
Clearly the woman's beliefs leading to emotional states and
action expressions of those states are not dependent upon all of the actual
facts. Similarly, even if her understanding of the facts is
correct, this still does not mean that her emotions have to be in line
with them. Consider the following alternate continuation to the story,
- Instead of playing the rookie quarterback, however, the coach put in
a third string quarterback instead. The woman, who unbeknownst to the
brother had consumed a case of beer with the rookie quarterback at lunch
and was sworn to secrecy about it, was relieved. The brother, however, felt
terrible for the rookie quarterback that he did not get to play and was
consequently very unhappy. The woman was sorry to see him in this
state.
In this case the woman knows that the brother's beliefs are
incorrect, and she does not share them, but she still is capable of having
emotions based on the brother's fortunes, which in turn are based on
those incorrect beliefs.
It can be seen then, that to accurately represent the fortunes
of another agent, to have emotions regarding those fortunes and to
accurately interpret their actions with regard to those fortunes, we
must not only represent the concerns of the other agent, but also his
representations of the concerns of those who are important to him.
The design of the construal mechanism for agents in the Affective
Reasoner allows us to create such twice-removed points of view.
Essentially, every distinct interpretive personality representation
is structurally and functionally the same. It does not matter whether the
representation is to be used as a GSP by the system, or as a COO or
satellite COO by the agent. Thus the emotion machinery that applies to GSPs
for the generation of direct emotions will also apply to COOs used for the
generation of the fortunes-of-others emotions and to satellite COOs used for
representing an agent's beliefs about another agent's beliefs.
The process for making use of each of these GSP and COO databases is
essentially the same in all cases. The eliciting event, act or object is
filtered through each respective GSP or COO database to produce an
interpretation with respect to the antecedents of emotions. In the direct
case, this will lead to emotions the system generates for the agent. In the
once-removed case it will lead to an interpretation based on imagining what it is like for the other agent (possibly incorrectly),
which, when combined with a relationship may yield a fortunes-of-other
emotion in the observing agent. In the twice-removed case, when combined
with beliefs about relationships, the interpretation may lead to a belief
about the emotional state of the other agent, which in turn may also then
lead to fortunes-of-others emotions.
2.8.4 Defaults
In some cases little may be known about another agent. Nonetheless,
one may feel sorry for a stranger, and one certainly may wish to explain the
actions of strangers. Thus we must give agents a mechanism by which they
may still reason about the emotions of other agents, even if
little is known about them.
Since, for the purpose of generating emotions in the Affective
Reasoner, one GSP database is as good as another, and since even the
component construal forms may be mixed at will, we may use a system of
defaults for reasoning under uncertainty. Two of these are rather obvious.
The first is a system-wide default GSP which corresponds to the knowledge
source one might consult
in addressing such questions as, How might a typical agent interpret this event? The
interpretations produced by such a default database are useful
when producing explanations such as, when someone is hit they get mad
and losing money increases distress. The next obvious default GSP is
an agent's own GSP database, which corresponds to the knowledge
source one consult's when asking, How would I interpret and react to
this event? The resulting emotional states can then be projected onto the
other agent.
After these two defaults, the path is less clear. In the Affective
Reasoner, when modeling some new agent, an agent may make use of a
previously existing COO for some third agent as a default.
As discussed in section 1.5, as long as this
COO is suitable it remains in use. When the COOs diverge (i.e., when one of
the construal forms in the existing COO is found to be incorrect for the new
modeled agent) then a copy of the existing COO is made and the offending
construal form is removed. This becomes the current representation of the
COO for the new agent. The use of COOs in this manner corresponds roughly to
reasoning that since Agent A seems just like Agent B, then
assume he is alike in all ways until learning differently.
Nor are we restricted to using only one COO when searching for an
explanation. For agents then, the order of precedence is as follows: (1)
search through the COO for the other agent to look for an interpretation of
some eliciting situation; if there is none, or it is found to be in error,
then (2) search through some other COO for an agent that appears to be
similar to this one, if one exists; next, (3) search through the system
default GSP database to see how a typical agent would interpret the
situation; failing in this, then (4) search through one's own GSP database
to see how I might interpret this situation, and lastly (5) search
through the global shared database of construal forms for all possible
interpretations of the situation.
Figure 2.16:
Selecting and adapting an interpretive personality structure for
another agent
 |
Building the frame-hierarchies that represent the concerns of other
agents is an iterative process. Figure 2.16 diagrams
this process, as an observing agent iteratively builds his representation of
some other, observed, agent. He starts with a default Interpretive
Personality representation provided by the system. As long as
interpretations of situations filtered through this construal database are
consistent with the observed features, and as long as the ``teacher'' of the
system has not complained, then no changes are made. It is not until some
construal is found to be in error that we make changes to the database.
In this example, the original default personality is given
as the abstract type 
. This produces the suitable construal of 
for a situation of type (e.g., situation type might be getting a
speeding ticket, construal for might be having a retain-money goal
blocked), and the suitable construal, 
, for a situation of type 
. A
construal of type 
is not suitable for situation type
however, presumably because it does not lead to the affective state believed
to have resulted from an evaluation of the situation. This initiates a
search in the global repository of construal frames for an interpretation
that will lead to the known affective state. The observing agent finds a
construal frame, 
, for situation type which does produce the
correct affective state, and replaces (or possibly complements) the
existing construal frame .
Rather than do this outright, however, the observing agent first
searches for an existing personality type that contains both the frames
already used correctly and the new one as well. As shown in the diagram, he
is able to adopt a personality of type 
that has these properties.
Following a similar episode involving the situation type he has to adopt
a personality of type 
. Finally, he must create a new personality
type, 
to model the observed agent. In the Affective Reasoner
default personality types may be paranoid, stingy, altruistic and
so on.
Figure 2.17 gives an overview of the processes the
Affective Reasoner goes through as an agent builds COO representations for
other agents. Below are annotations for nodes in the illustration:
Figure 2.17:
Reasoning from cases to build Concerns-of-Others representations
 |
- 1, 2, 3. Features, derived from the eliciting situation
and from response action generation, are read by a feature reader and
formatted as a case for Protos.
- 4, 5. Protos analyzes the case and classifies it as belonging
to one of the twenty-four emotion classes. A case-library is developed
through interaction with the user and features are placed in a
conceptual structure indicating their relationship to the emotion
classification. Output from Protos is, conceptually, an abduction of
an emotion that would plausibly explain the features. This is mode two.
- 6, 7. In mode one the abduction step is skipped and the
observed agent is directly queried about which emotion lead to his actions.
- 8. The hypothesized emotion is passed to the module
that maintains Concerns-of-Other (COO) representations for ``other''
agents.
- 9 - 16. The eliciting situation is filtered
through the ``eyes of the other agent'' using the COO representation for
that agent. If the resulting emotion is consistent with that passed from the
abduction module, then the COO is assumed correct, and no further processing
is done. If no interpretation is made by the COO, or if the interpretation
made is not consistent with the emotion passed from the abduction module,
then the COO is updated. A search is made through first default COOS, and
then a global database of all construal forms, for another construal frame
for the situation that will produce an interpretation matching the abduced
emotion category. When one is found it is used to update the COO for future
processing. The eliciting situation type is used as a simple index
into the various GSP (COO) databases.
In this chapter we have examined the fundamental concepts in the
Affective Reasoner. We have traced its processing of emotion eliciting
situations from the moment of instantiation by the simulation engine through
interpretation by agents, through the generation of emotions based on those
interpretations, and through emotion-induced actions. Two subjects covered
in detail in later chapters, construal and response actions
were introduced. In addition, a number of shorter topics were treated in
full, including an introduction to the emotion elicitation theory of Ortony,
et al. [18], the building of emotion eliciting condition
relations (EEC relations), the Affective Reasoner's representation of
multiple and compound emotions, reasoning about emotion episodes by
observing agents, and the construction Concerns-of-Others (COO) databases by
which agents represent their beliefs about the personalities of others.
3 Construal
A construal of a world event is an interpretation of that
event with respect to the concerns of some agent. Construals can vary from
one agent to another. For example, a last-second touchdown in a football
game may be seen as a great moment by a fan of the winning team, as a
disaster by a fan of the loosing team, and as of no concern to someone who
has no interest in football. In the Affective Reasoner a construal is
represented as a generalized internal schema (i.e., a construal
frame) bound to a ground instance of some eliciting situation
frame.
In this section we look at an extended example of a such a touchdown
situation in a football game from the point of view of three different
agents Tom, Dick and Harry. We examine several different
instantiations of the situation and trace the many different ways each can
be construed by the three agents, leading to an array of distinct
emotions.
Represented in figure 3.1 are three different construal
frames used to interpret the first scenario of the football situation, one
for each of the three agents, Tom, Dick and Harry. These three construal
frames are matched against the situation frame (event-262) leading to the
three different construals (i.e., of goal-achieved, goal-blocked,
and preference encountered.
As can be seen, Tom is a Northwestern football fan. Most touchdown
situations are of interest to him if one of the teams playing is
Northwestern. Specifically, the construal frame shown will only be invoked
if Northwestern is playing and there is almost no time left on the
clock.If Northwestern scores a last-second touchdown Tom's heroic-finish
goal will be achieved and may generate joy. On the other hand,
should Illinois score the last-second touchdown, then Tom's goal would be
blocked and he might experience distress.
Figure 3.1:
Tom, Dick and Harry construe a last-second touchdown.
 |
Dick is an Illinois fan. His construal of the situation will mirror
Tom's: if Northwestern wins, his goal is blocked; if Illinois wins,
it is achieved. It is important to note that any instantiated situation of
this type that is of interest to one of the agents, Tom or Dick, is of
interest to the other. Furthermore, the situation would be of similar
interest (the game was decided in the last second with a heroic finish;
someone won and someone lost), although the interpretations of the situation
with respect to the heroic-finish goal would be diametrically
opposed.
The third agent, Harry, on the other hand, has no interest in the
touchdown, per se. He is, however, interested in the situation. His
entertainment preferences run to sunny days and festive crowds. Since the
situation meets both of these requirements he may experience an enjoyment
emotion.
Here is a walk-through of the matching process for the agent Tom:
- the type of eliciting situation frame event-262
is touchdown so construal frames which interpret situations of type
touchdown are retrieved from Tom's interpretive personality
database. In this case there is only one construal frame for interpreting
touchdown situations.
- ?time-left, ?t1, ?t2, ?t1s and ?t2s are all bound to
the appropriate slot values. Note that this is the simplest type of match
(other than constants) but that any type of match template is allowed
and that a standard unification procedure is used. Bindings are passed along
to the next slot and unification must proceed with the new constraints.
- A predicate function is called on the substitution of the given
inequality. That is, the variable ?time-left is replaced by its value
in the set of bindings and the inequality is evaluated. A true value
allows the match to continue, a false value terminates the process
and justifies an unsuccessful match.
- A predicate function is called on the substitution of the next
expression which tests for either ?t1 or ?t2 being bound to the
constant Northwestern. If this is the case then the
predicate succeeds, otherwise the match fails.
- blocked is assigned a value of true or false depending on who
wins the game. In either case the situation is of concern to Tom. If the
goal is blocked this situation may produce a negative emotion, if it is
achieved it may produce a positive emotion. Note that although it is not the
case here, for some goals there is an intermediate value which says that the
goal is neither blocked nor achieved. For example, suppose a taxi driver
gets a fifteen percent tip. Since this is the usual amount, the chances are
that he will be neither happy nor unhappy about this. A construal frame for
interpreting a get-tip situation would not match unless the tip were
significantly less or more than normal. This is a an important point for
content theory development. One might ask, if the goal is neither blocked
nor achieved then who cares if the eliciting situation is otherwise relevant
to this goal frame? As it turns out, however, it is useful to consider such
situations to be of interest to the agent (since all other features of
the situation are matched) but not of concern with respect to the
goal. This is relevant when we consider expectations with respect to
goals (see section 3.3 below).
Inherited information may come from the GSP frame hierarchy.
This is useful when we want to add interpretive value to a set of
construals, value which does not come from the situation itself. In this
case we see that this heroic-finish goal is a football goal. We might
then wish to pass along the information that this is not a very
important goal in the larger arena of life, or that it is a sports goal, which might affect the nature of how we express our
emotions about it.
The construals (i.e., the bound interpretation forms) produced by
interpretations on the part of the three agents shown in
figure 3.1 are used to create the Emotion Eliciting
Condition relations described in section 2.3. Going back
to Tom's construal of Northwestern's last-second heroic finish we have:
| self |
other |
desire- |
desire- |
pleased |
status |
appraisal
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Tom |
none |
desire |
none |
none |
none |
none
|
none |
none |
|
Emotion Eliciting Condition Relation for Tom with respect to
Northwestern's last-second touchdown.
Assuming that there were no threshold or other calculations that
aborted the construal process, this configuration of the Emotion Eliciting
Condition relation would lead to an affective state of joy, since
being pleased about an event that is desirable (for one's self) meets
our preconditions for that state. On the other hand, For Dick, the Illinois
fan, we have:
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Dick |
none |
undesire |
none |
none |
none |
none
|
none |
none |
|
EEC Relation for Dick with respect to Northwestern's
last-second touchdown.
This construal, subject to the above constraints, leads to distress, since being displeased about an event that is undesirable
(for one's self) meets the preconditions for that state.
Now let us change this situation slightly so that the confirmation
status of the situation comes into play. Suppose, for example, that
Northwestern were to appear to have scored the last-second go-ahead
touchdown as in the above story, but that this time the officials are
meeting to discuss whether or not the ball-carrier had actually crossed the
goal line. The outcome of the game is on the line. In this case the status
is unconfirmed until the officials make their final ruling. Tom and
Dick both believe that it is likely that the touchdown will stand. In this
case the construals produced from the situation matching process for each
agent will be the same as before, except that the status attributes will be
changed from none to unconfirmed. Thus the EEC relation for Tom
would be:
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Tom |
none |
desire |
none |
none |
unconf. |
none
|
none |
none |
|
EEC Relation for Tom with respect to Northwestern's
unconfirmed last-second touchdown.
leading to hope. The EEC relation for Dick would be:
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Dick |
none |
undesire |
none |
none |
unconf. |
none
|
none |
none |
|
EEC Relation for Dick with respect to Northwestern's unconfirmed last-second
touchdown.
leading to a fear emotion (e.g., worry or concern).
3.3 The confirmation emotions
One of the central issues to consider when thinking about the
confirmation emotions is that in reality the goals and expectations on which
they are based are constantly changing. An expectation that one's team is
going to lose the game may alter one's goals: anything other than losing
(e.g., tying, or having the game called) can now become a desired outcome.
In addition, expectations may vary in strength (i.e., possibly
versus extremely likely). A proper treatment of these problems is
beyond the scope of this research. Instead we have made some simplifying
assumptions that have allowed us to implement a pragmatic form of reasoning
about the confirmation and disconfirmation of expectations. In this section
we discuss this approach.
To continue our example from the previous section, let us look at
what happens when the status of some situation which was previously unconfirmed becomes either confirmed or disconfirmed. To do
this we look at what happens when the officials make their final ruling. If
they decide that the touchdown stands, then Tom's and Dick's expectations
are confirmed, leading, respectively, to the satisfaction and
fears-confirmed emotions; if the officials decide that the touchdown
is to be called back, causing Northwestern to lose the game, then Tom's and
Dick's expectations are disconfirmed leading to, respectively, disappointment and relief.
Let us consider the implications of implementing such a scheme. The
critical issue is how to handle the transition state between a situation
being unconfirmed and it becoming confirmed or disconfirmed. To illustrate this using our example, suppose that
the belief about the last-second touchdown was established much earlier than
just before the officials were to rule. We might consider, for example, that
the last three times these two teams had met, Northwestern had won the
football game with a touchdown in the last second. Now, at the
beginning of play in the first quarter, Tom and Dick are, respectively, hopeful and fearful that Northwestern will again win in a heroic
finish. The problem then is to decide when, if ever, this expectation has
been met or shown to be invalid.
In the Affective Reasoner all action is controlled by the simulation
queue. This means that (dis)confirmation of expectations must be initiated
by a simulation event. If the simulation event gives rise to a confirmed or
disconfirmed version of the expected situation then the expectation is
retired and a confirmation emotion is generated. We call this active
confirmation or disconfirmation. On the other hand, the expectation might
be retired by a simulation event that does not give rise to the expected
situation (e.g., is retired by a time limit). This might simply cause the
expectation to disappear (i.e., as though forgotten) or to confirm or
disconfirm the expectation, giving rise to a confirmation emotion. In such
cases we refer to this as passive confirmation or disconfirmation.
These two approaches are discussed in this section and the next.
To illustrate, consider again the football example. Suppose that
expectations have been set up because Tom (the Northwestern fan) and Dick
(the Illinois fan) both believe that Northwestern will win the football game
with a touchdown in the final seconds. A process that performs active (dis)confirmation must examine all situations that arise
subsequent to the creation of the expectation to see if they apply. Since
most situations do not apply, it must filter them out. For example, when
looking for (dis)confirmation of the last-second-finish expectation it must
filter out any situation which is not of type touchdown, since a
touchdown situation is what Tom and Dick are anticipating. It must also
filter out all touchdown situations that do not take place at the end of the
game, since only those that do are relevant to the expectation. Lastly it
must filter out all last-minute touchdowns that do not change the outcome of
the game such that Northwestern wins.But we have seen this before: the active matching of expectations is exactly the same process as that of matching concerns. Every eliciting
situation frame must be considered, at some level, as a potential
(dis)confirmation of the expectations that have been created by the agents
in the system, just as every eliciting situation frame must be considered
as a potential initiator of emotions.
This is, in fact, exactly how active (dis)confirmation of
expectations is treated by the Affective Reasoner. To implement this, it
would seem that a separate database of expectations (i.e., one designed to
be used as match schemas for eliciting situation frames and modeled after
the GSP hierarchies of agents), would suit our purposes. However, it is
clear that every expectation frame is derived from a previously instantiated
GSP frame, specifically a goal-based one. This means that instead of
building a separate hierarchy of frames for the expectations that have been
created we can instead use the original GSP hierarchy and simply keep
auxiliary information to supplement that database. Clearly, if a situation
can be unified with an instantiated expectation frame then it can also be
unified with the original GSP schema (i.e., construal frame) from which the
instantiated expectation frame was derived. This means then, that we may
greatly reduce our processing by considering for expectation matches only those situations which have been shown to match the original concern
from which the expectation was derived. Since concern matching must be done
anyway, we have saved processing expense by using it also as a filter for
expectation matches.
Here then is the general idea. We match some original emotion
eliciting situation and get an expectation frame which is implicitly
represented as a set of bindings from the original construal. If we can
match the original construal frame again, within the constraints of these
bindings, we may be able to resolve our expectation. To illustrate, we may
imagine a situation in which Tom tells Dick, the Illinois fan, that
Northwestern almost always manages to win by scoring a touchdown in the
final seconds. This sets up an expectation in Dick, represented as a set of
bindings for the status-unconfirmed touchdown situation. Later, when an
actual touchdown situation arises, an attempt is made to unify the bindings
created from matching the eliciting situation frame against the
touchdown-event construal frame with the bindings representing the
expectation. If the two sets of bindings can be unified then a
(dis)confirmation emotion is generated, such as relief that Northwestern did
not manage to beat Illinois in the final seconds, or having Dick's fears
confirmed that Northwestern did, in fact, win in the final seconds.
Otherwise, if the construal frame matches but the bindings are not
compatible with those representing the stored expectation frame, then a
direct emotion (such as distress) or prospect-based emotion (such as hope)
is generated.
Figure 3.2 illustrates this for the general case. Here we
see that expectation frames are stored as a set of bindings and a pointer
back to the original construal frame that generated them. When, later, a
new eliciting situation of the same type (in this case type s-37) is
generated it is matched against the appropriate construal frame. In
addition, since there has been an expectation generated by an interpretation
previously made by this construal frame (i.e., the implicitly represented
expectation frame stored in bindings set 4, for situation
41), additional unification is performed to see if the bindings generated
from the current match are compatible with the bindings from the previous
match. Depending on the results of this additional unification a
(dis)confirmation emotion may be generated.
Figure 3.2:
Maintaining expectation lists for the confirmation emotions.
 |
To illustrate why these original bindings are necessary, consider
the following, which might happen if the original bindings were ignored:
- Tom is fearful that Harry, whom he does not like, will
also show up at the game. However, Harry has given his ticket to Dick,
whom Tom does like. When Dick shows up, Tom's fears that Harry
will show up are confirmed. [sic]
This could occur if the bindings, which specify that the agent expected to
show up is Harry, were ignored. Thus the match must be consistent
with the bindings that instantiate the original expectation. However, some
modifications are necessary because we do not really want to match the
original situation again, but rather some similar situation which
takes place in the future. The bindings for the two situations will differ
in three areas: (1) in some specialized bindings such as temporal features,
(2) in the status value of the situation, and (3) in the value of ?blocked-violated. We now take a closer look at these three modifications
to the stored bindings:
- Specialized bindings. Suppose that some eliciting situation
arose at time
and that it was construed as an unconfirmed
situation, so that an agent was worried (or hopeful) about it. Later, at
time 
a situation of the same type and with the same features occurs
but is construed as confirmed by the agent. Since the features are
the same, the match between the expectation frame and the construal of the
new situation almost succeeds, but ends up failing because 
. To
avoid this problem in the Affective Reasoner the matcher is modified so that
members of a specified set of features (such as ?time) are not
required to participate in the unification process. In our example, the
match would now succeed and the 
situation would lead to one of the
confirmation emotions on the part of the agent.
- Status attribute. In the original eliciting situation taking
place at
the status attribute was bound to the constant, unconfirmed, leading to the creation of an expectation. However, when
(dis)confirming this expectation the status attribute must be matched in a
special way, in fact using the exact opposite of the normal matching
procedure. In this case a successful unification of the constant unconfirmed would cause the match to fail, while either confirmed or
disconfirmed would cause it to succeed. Intuitively this is correct:
if the situation is (still) unconfirmed it will not help to resolve the
expectations raised by the original unconfirmed situation; if it is confirmed or disconfirmed, it may.
- ?blocked-violated value.
We have already indicated that some goals and standards may, in
addition to specifications for blocked and achieved, have a
third specification of neither blocked nor achieved, i.e.,
nil. Here is where this becomes important. A ?blocked-violated
value of nil is a good outcome causing relief when one is in a
state of fear, since the expectation that the goal will have been
blocked has been disconfirmed. On the other hand nil is a bad outcome
causing disappointment when one is a state of hope since the
expectation that the goal will have been achieved is now disconfirmed.
In all cases, if the value of ?blocked-violated in the stored
bindings for the expectation frame is matched exactly in the new situation
frame, the expectation is confirmed, otherwise it is disconfirmed.
For example, suppose that Tom has the goal of having his team win.
Consider two scenarios. In the first he is expecting his team to
loose, and consequently is fearful about this outcome. In our
implementation this means that the construal frame for this goal has matched
an unconfirmed version of this situation, where the goal has been
blocked, leading to both the generation of the prospect-based emotion of
fear and the creation of an expectation frame. If later the game
ends in a tie, then Tom will experience relief because the
expectation of a negative outcome has been disconfirmed. This
disconfirmation arises
because the value of ?blocked-violated in the expectation
frame (i.e., blocked) does not match the value of ?blocked-violated in the actual outcome (i.e., nil).
In the second scenario Tom is expecting his team to win, and
consequently is hopeful about this outcome. Similar to the above, we
represent this as an unconfirmed construal, leading to the generation of the
prospect-based emotion of hope and the creation of an expectation
frame. If the game later ends in a tie then the disconfirmation of Tom's
expectation will lead to disappointment.
These two examples illustrate how, in the Affective Reasoner's
paradigm, a goal-relevant situation that ordinarily would not lead to an
emotion can still lead to relief and disappointment through the
confirmation or disconfirmation of a prior expectation.
Now we look at the harder problem, that of (dis)confirming an
expectation passively (i.e., by default). Going back to our football
example, where the agents have formed the expectation that Northwestern will
score a touchdown in the final seconds, we can see that if the game is over
and this has not occurred then the expectation has been disconfirmed by
default. We need only set a time limit on the
expectation equal to the end of the game and at that moment, if nothing has
happened to intervene, expire the expectation. We do this by putting a
simulation event into the simulation queue equal to that time. This
simulation event then gives rise to an emotion eliciting situation at the
appropriate time which in turn leads to the disappointment and relief emotions of Tom and Dick, respectively.
What are the problems with this approach? First we have a simulation
detail to consider. Clearly an expiration event must be created within the
simulator. This event must necessarily take an active role in causing
the (dis)confirmation emotions, and it must point back to the original
construal frame creating the expectation. In this case the end of the
football game must somehow be linked to an expectation for a touchdown
situation, two possibly unrelated frames. This can be a messy process.
Next, consider the related problem that since the time when the
football game will end may not be known in advance (i.e, if the simulation
events representing the game are created dynamically as the game
progresses), the end-of-football-game event cannot be placed
directly in the time-sorted simulation queue. Some fix is required, such as
an end-of-football-game cleanup event. As just discussed, this simulation
event would have to point to expectation frames (i.e., construal frames)
that were to be activated or deactivated when the football game ended. But
this means that the events in the simulated world might have to be modified
every time some new relevant construal frame is added to a personality. For
example, suppose that we decided to make one of our agents worried about
traffic when the football game was over. The end-of-football-game
simulation event would now have to be altered to trigger (dis)confirmation
of this additional worry. This is clearly not an attractive solution.
Now, consider the hardest problem of all, that most cases of passive
(dis)confirmation are not associated with a specific time at all.
Instead, the prospect-based emotions just fade away over time. Suppose, for
example, that rather than fear that one's football team is going to lose
this game, one has a more generalized fear that his team is going to
let him down. When will enough games have passed that this fear is allayed?
Two games? Four games? Six minutes and five seconds into the first quarter
of the fourth game,...?
Figure 3.3:
Prospect-based and confirmation emotions
 |
Lastly, we also must consider cases where the prospect-based
emotions end abruptly because some other emotional episode occurs and takes
precedence. If Tom learns that his mother is dying, for example, he is
unlikely to continue experiencing anxiety over the outcome of a football
game. In this we see the essence of the problem: For the active
(dis)confirmation of situations we can effectively look for an occurrence of
the situation we are expecting, in either the blocked-goal or achieved-goal
(and possible nil) versions. For passive (dis)confirmation we
have no way of knowing what it is that will trigger interaction with the
expectation, yet this interaction must be initiated by that unknown
situation leading to ``awareness'' on the part of the agent.
How is this handled in the Affective Reasoner? Active
expectation (dis)confirmation is handled using the matching process as
specified, and by keeping a list of currently active expectations for each
agent. Passive expectation (dis)confirmation is simply by time limit.
A simulation event is created to expire the expectation at a certain time in
the future. If the prospective situation has already been (dis)confirmed
then nothing happens, otherwise (dis)confirmation takes place by default,
possibly subject to some additional constraints. This difficult area is ripe
for a detailed structural analysis, but is currently beyond the scope of
this research.
Figure 3.3 illustrates our use of expectations in
generating emotions. A situation may be unconfirmed and so
give rise to the prospect-based emotions of hope and fear. At the
same time, this will create an expectation, which is stored as a
set of bindings from the original construal. Later, if the situation
is either confirmed or disconfirmed this will give rise to one of the
confirmation emotions, depending on the actual (dis)confirmed situation
and the prior expectation about it.
In this section we continue to examine different ways in which the
last-second touchdown situation may be construed. We have seen how
such a touchdown situation can be relevant to goals both directly and in
terms of prospective outcomes. We have also seen how simple preferences can
be invoked and how different perspectives can be used to interpret the same
situation. Now let us turn to construals based on interpretations of
eliciting situations as being the praiseworthy and blameworthy acts of
agents, i.e., the standard-based appraisals of eliciting situations.
To this end, let us imagine that the Illinois coach has said the
following to his team just before the last play of the game: ``I am sure
that they are going to run the football on the right side. We could fade
back into a zone defense and probably stop them before the end zone, but I
want to show them our stuff and beat them on the line.'' When Tom and Dick
see the way the defense lines up they both know that the coach is taking a
needless chance just to make a macho point to the Northwestern team. The
defense fails and Illinois loses a game they might otherwise have won.
Tom, the Northwestern fan, is happy that his team has won. In
addition he is scornful of the Illinois coach's decision which he
felt was stupid. The EEC relations for these interpretations are:
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Tom |
none |
desire |
none |
none |
none |
none
|
none |
none |
|
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Tom |
none |
none |
none |
none |
none |
blame
|
none |
coach |
|
EEC Relations for Tom with respect to the Illinois
coach's blameworthy act, violating Tom's standard but achieving his goal,
leading to the mixed emotions of joy and reproach.
Such a relation would lead to mixed emotions: (1) joy over winning the
game (as discussed above), and (2) reproach over the violation of one
of Tom's standards, namely the win-at-all-costs standard that he feels
all football coaches should have.
Dick (the Illinois fan), on the other hand, is not happy
about his team losing the game. Nonetheless, he respects the coach
for playing what he considers to be in the true macho spirit of the
game. The EEC relations for these interpretations are:
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Dick |
none |
undesire |
none |
none |
none |
none
|
none |
none |
|
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Dick |
none |
none |
none |
none |
none |
praise
|
none |
coach |
|
EEC Relations for Dick with respect to the coach's
praiseworthy act, upholding Tom's standard but blocking his goal, leading to
the mixed emotions of distress and admiration.
In this case Dick has mixed emotions: (1) distress over
losing the game, and (2) admiration for the Illinois coach for
upholding his (Dick's) standard.
Now let us suppose that the standards held by Tom and Dick are
reversed, with Tom adopting the standard that coaches should ``stick it to
them'' whenever possible, and Dick the standard that coaches should ``win at
all costs''. In this case the Illinois coach's decision was praiseworthy from Tom's point of view, and blameworthy from Dick's
point of view. Since a standard has been upheld and a goal achieved, the
preconditions for both joy and admiration have been met on Tom's
behalf. However, taken as a whole, the preconditions for the compound
emotion of gratitude have also been met, and this takes
precedence.Likewise, since a goal has been blocked and a standard violated on Dick's
behalf, the compound emotion of anger takes precedence over distress and reproach. Here is the EEC relation for Dick's anger:
| self |
other |
desire- |
desire- |
pleased |
status |
evaluation
|
appeal |
responsible |
|
|
|
|
self |
other |
|
|
|
|
agent |
|
|
Dick |
none |
undesire |
none |
none |
none |
blame
|
none |
coach |
|
EEC relation for coach's blameworthy act blocking to Dick's
goal, leading to anger.
In figure 3.4 we see the two construal frames
that lead to this EEC relation and how they match the original situation. As
can be seen, some of the features in the original situation are not relevant
to either of these particular concerns (e.g., weather and crowd-noise), and some are relevant to only one (e.g., time-left
is important to the goal of having a heroic finish, but is not important to
the standard dictating a win-at-all-costs style of play). In addition, some
of the construal frames capture bindings for features that are not used in
the construal process (e.g., team1 and team2 in the second
construal frame). These bindings may be used later, however, when forming
action-responses.
Figure 3.4:
Dick construes situation in a way that leads to anger.
 |
Table 3.1:
Compound Emotions
|
Goal |
Standard |
Emotion |
|---|
| achieved |
upheld |
gratification (self), gratitude (other) |
|
blocked |
violated |
remorse (self), anger (other) |
In general, each construal frame that matches an eliciting situation
frame will produce its own Emotion Eliciting Condition relation. If one
eliciting situation is of concern to an agent with respect to three
different goals or three different preferences, etc., then three EEC
relations will be produced, from which in turn may be derived three
different emotion instances, even of the same type. This holds in all
cases but for those EECs producing compound emotions (such as the examples
of gratitude and anger above). In the first case a single
construal produces a single EEC. In the latter case each pair of
construals that belong to the relation illustrated in
table 3.1 produces a single EEC relation, as discussed
in section 2.4.6. Briefly, there will be 
EEC
relations for compound emotions where is the number of standards
construal frames matching an eliciting situation frame, and 
is the
number of goals construal frames matching the eliciting situation
frame, subject to the above constraints.
We have seen how Tom and Dick can have multiple and compound
emotions in response to a single situation. To close this section we now
take this one step further and see how a single situation can lead to conflicting emotions. Suppose, for this argument, that Tom figured that
Northwestern would lose the football game. Banking on this he bet against
them. Now, although he is happy that his team won in such a dramatic
fashion, he is also distressed about losing his bet. On the one hand a goal
of his has been achieved, on the other hand a different goal has been
blocked. Figure 3.5 illustrates this.
Figure 3.5:
Tom's multiple construals lead to joy and
distress.
 |
Many situations can be interpreted in either a positive or a
negative way by an agent, depending on the bindings for only a few of the
many constituent features of the situation. For example, suppose the
following situation arises in the life of a taxi driver: He delivers a
passenger at some location. The ride was fine. He gets paid. In most cases,
since he wants to make money, if he gets a big tip he will be happy and if
he gets a small tip he will be unhappy.
In the scheme discussed so far in this chapter, these opposing, but
otherwise similar, construals (e.g., of the tip situation) would each be
represented as a distinct construal frame: one leading to a positive emotion
and the other leading to a negative emotion. Obviously, however, both
situations share many features. To make use of this in the
Affective Reasoner we have, in general, represented both situations with the
same construal frame, but have added a procedure to calculate the value of
an additional attribute, blocked-violated, representing the blocking
of goals or the violation of standards. Thus the match process may be
thought of as containing two steps. The first is determining whether or not
a situation is of interest to the agent. The second is determining
the valence of the construal. This valence is represented as a true,
false, or nil value for the ?blocked-violated attribute
where nil may indicate either a positive or a negative construal
depending on prior expectations.The blocked-violated function that computes this value does not
participate in the determination of relevance, it only adds a value for the
?blocked-violated variable to the bindings which are used to create
the Emotion Eliciting Condition relation.
Representing positive and negative construals in this way has the
benefit of halving the computational load involved in the matching process
by avoiding the unnecessary duplication of data. However, this solution is
not without problems of its own. Some goals can only be achieved (and some
standards only upheld) and never blocked (violated). Other goals can only
be blocked (standards violated) and never achieved (upheld). We call these
serendipity and preservation goals and standards,
respectively. They are related to the idea of preservation goals
([26]) but are simpler and are driven by more practical
concerns. When the match process is performed using a serendipity construal
frame, if ?blocked-violated is bound to false (i.e., not
blocked) the match continues, else it fails. When the match process is
performed using a preservation construal frame, if ?blocked-violated
is bound to true (i.e., blocked) the match continues, else it fails.
Here is a story that illustrates the need for a serendipity goal:
- A wealthy-looking stranger walks up to John on the street and gives
him a thousand dollars in cash, explaining that he just likes to make
people happy. John was overjoyed.
In this case John experiences joy because he has a goal of getting
money. On the other hand, if the stranger does not give him a thousand
dollars he would not experience distress. Here is a story that
illustrates the need for a serendipity standard:
- John leapt out from the crowd, rushed into the raging fire,
and pulled the strange dog out to safety. Sarah admired him for
his bravery.
Sarah has a standard that says that endangering one's self for the benefit
of others is worthy of approval. However, if John did not rush into the fire
Sarah would not have blamed him for it. Here are two stories that
illustrate the need for preservation goals and preservation standards,
respectively.
- John is walking down the street, and a flower pot falls on his foot.
John is unhappy about it.
In this case John experiences distress,
because he has a (preservation) goal of maintaining his good health. On the
other hand, if he walks down the street and the flower pot does not
fall on his foot, he is unlikely to experience happiness about it.
- John is walking down the street when he sees two strangers passing
each other on the next block. The first stranger robs the second
stranger and then disappears. John is reproachful about the robber's
evil actions.
In this case John feels reproach for the robber. On the other
hand, should the two strangers have passed without incident then John is
unlikely to feel admiration for the would-be thief.
One might argue that this is really a matter of the surprisingness of the eliciting situation, and that if an outcome is usual it should not produce a valenced reaction, (see [19] and
[16] for a discussion of this). In the previous examples such
a treatment would suffice. However, there are cases where the surprisingness approach is not as suitable. Here is a story that
illustrates this:
- John is looking out his window and sees a neighborhood youth walking
down the street eating candy. Usually the youth throws his candy wrapper on
someone's lawn. This time, as is no surprise, when the youth passes
a neighbor's house he does exactly that, again. John is reproachful.
Here John experiences reproach for the youth, because John has
a standard about littering. On the other hand, if the youth were to put
the trash in his pocket John would be surprised but would probably not
experience any emotion at all. In such a case, it is the unsurprising behavior that leads to an emotion, and the surprising
behavior which leads to none.
In this chapter we have discussed a touchdown event
at a football game from a number of different points of view.
This example was used to explore the goal-based and standards-based emotions
and to discuss how multiple and even conflicting emotions can
arise for a single agent. The difficulties in representing expectations was discussed in light of the prospect-based emotions
of hope and fear, and the confirmation emotions of relief, fears-confirmed,
disappointment and satisfaction. The mechanism used to store expectations
was discussed. Lastly, special classes of goals and standards which
lead only to either the positive or negative emotions, but not
both, were discussed and the serendipity and preservation
construal frames used to interpret them were introduced.
4 Response Actions
When we think of someone as ``being emotional'' we are usually
referring to their thoughts and actions following some emotion-inducing
situation. These response states are initiated and controlled in the
Affective Reasoner by the manifestative personality component. Few
attempts have been made in AI to decompose emotion-based action, probably
because of the difficulty of the task. Here are some of the obstacles to an
effective treatment of this domain.
First, what are the functional considerations of response-action
processing? Are the actions to be merely expressive in nature, or are they
going to drive the system? Perhaps they will be used as modulators of a
logic-based action generation system? Each approach has merit, and the best
system will support them all. In one system, for example, we might use
emotion feedback only to adjust the communication levels between agents. In
another we might instead implement agents so that they only act
when driven to do so by their emotions.
Second, we must decide on the complexity of the actions that we are
going to implement. Emotional responses may be as simple as turning red
in the face or as detailed as planning revenge against a traitor.
A full treatment of the abstract emotion domain must embrace a wide spectrum
of such responses.
Third, if agents are engaged in purposeful behavior it can be
assumed that they are guided by some implicit or explicit plan. We must ask
ourselves then: How can emotion-based action help agents to further their
goals? What is the purpose of the emotion machinery and how can its
effectiveness be measured? If we define the result of planning simply as the
sequencing of actions in time, the question is obvious - how do we
integrate emotion-based action into that sequence such that the new sequence
is more beneficial to the agent (or to the society of agents as a whole)
than was the old sequence?
Lastly, we must consider taxonomic issues. Smiling can be
an appropriate action response when one feels joyful. It can also be
an expression of pride, of being happy-for someone, and so
forth. In addition smiling can mask the repression of negative
emotions such as when one smiles to cover for being sad. On the other
hand, other actions usually are associated with a particular emotion
(e.g., glaring at someone is associated with anger). The expression of
emotions is rife with such ambiguities: one emotion can initiate many
expressions, one expression can have many initiating emotions. In addition,
almost any action can serve the purpose of emotional expression, but
this is entirely dependent upon the context in which the action takes place.
For example, we might make friends with someone because we are lonely (a
problem-reduction plan-initiation emotion manifestation) or we might
do so simply because it is good for business (a purely non-emotional
motivation). In summary, then, we have a weak-theory domain, that
is, one where some relationships consistently hold (e.g., shaking one's fist
usually implies anger) but where most only occasionally hold (e.g., anger
does not entail shaking one's fist).
Figure 4.1:
Action Response Categories for Gloating
 |
4.2 The three-dimensional nature of the action database
Figure 4.1 (inspired by [13]) gives a breakdown
of the action response categories and their theoretical groupings for the
emotion gloating. The high-level categories (i.e., goal-directed, expressive, information-processing, and so forth), are not
presently used for action generation.They instead are employed simply as a way of organizing the spectrum of
responses. One possible use of the theory underlying these categories,
however, might be as a basis for a functional representation of emotions as
initiators of purposeful, motivated actions. In this regard, the information processing actions, for example, might be seen as representing
aspects of an agent's attempts to ``understand what is going on.''
The high-level categories are arranged, as far as possible, from
the more spontaneous to the more planned (e.g, expressive actions tend
to be less planned than those for emotion regulation and modulation).
Within these high-level categories the particular action response categories
are similarly arranged (e.g., somatic expressive responses tend to
be more spontaneous than communicative-verbal expressive responses).
Lastly, within the action sets themselves the tokens (and mini-plans) are
arranged from the least intense to the most intense (e.g., smiling as
a non-goal directed, expressive behavioral-toward-animate expression
of gloating is less intense than is laughing). In all cases,
these are at best partial orderings since different contexts will affect the
ratings of the categories and actions, and in any case the ratings are
purely subjective.
One important point to consider is that this in NOT an attempt to
list action words that might be associated with a particular emotion. If it
were then we might have the following sorts of entries for gloat:
boast, glory, delight, jubilate, exult, and so forth. While this would be
interesting with respect to understanding and generating text, it is not
useful when attempting to characterize some internal state. The point is
this: there are many synonyms for a single action expression of an emotion.
Since we are making the attempt to generate response actions that have some
(possibly abstract) purpose we are interested in reducing the
number of redundant emotion expressions, and in increasing the range of
purposeful emotion expressions allowed. Thus, while slapping some
inanimate object is a rather obscure way of expressing the gloating
emotion, it has more value to the system than the more understandable exult which is simply another way of saying crow.
The actual response actions themselves may be simple tokens, such as
flush, they may be templates requiring variable instantiation
such as laugh at ?other-agent, or they may be full plan
initiations such as plan for recurrence of event.Plans in the Affective Reasoner are simply ``canned'' responses since
planning is not the focus of this research. Nonetheless, emotionally
motivated plan invocation has been addressed, and at least may serve
as a starting point for the integration of other work.
Once an action has been selected it is instantiated using the set of
variable bindings generated by matching the original emotion eliciting
situation frame. For example, bindings may be accrued for ?other-agent
during the original match process. This binding may then be used to
instantiate the action response template laugh at
?other-agent, described above.
Figure 4.2 shows the full structure of the first portion
of the action response mechanism. As can be seen, the structure for all
twenty-four emotion categories is similar, although only that for gloating is shown here. In this example we see that superior smile
and throwing arms up in the air are two ways of non-verbally
communicating gloating and that inducing others to experience joy
at a victim's expense and inducing embarrassment are two ways of
modulating the emotions of others as an expression of the same gloating emotion.
Figure 4.2:
Structure of the Action Response
 |
As shown here, the action response space can be broken down into the
elements of a three-dimensional array. The first dimension is the
twenty-four emotions, the second is the twenty action response categories
and the third is the actions themselves. To understand the magnitude of the
action classification task, consider that given even a modest three action
tokens per response action category, we have 24 emotions times 20 action
response categories times 3 actions giving 1440 actions to be tracked
within the system. Even allowing only 3 actions to be selected as
simultaneous manifestations of some emotion we have 
action
combinations, not counting the almost limitless number of instantiations of
templates containing variables.
Having laid the foundation for expressing many different action sets
in response to emotions, we now need a method for grouping these responses
into those that are typical for the personality we wish to represent. At a
minimum we want to be able to represent such broad characterizations of
temperament as shy and outgoing. Better yet, we would like
to have an even more specific characterization. For example, we might wish
to be able to represent an agent who tends to be obsessively focused on
other agents' views of him, who tends to have somatic responses to stressful
situations, who rarely initiates plans in response to his emotions, and so
forth. One way to think of this is to imagine an actual person within the
context we wish to simulate and then describe how he might respond to
different situations. The twenty or so action response
categories provide a reasonable set of options for making such a
description.
For example, an agent might be described roughly as highly
excitable, warm, insecure, illogical and shy. For such a personality
we might choose to emphasize the temperament traits somatic -
positive and negative emotions (highly excitable), behavioral (towards
animate) - positive emotions (warm), and evaluative (self-directed
attributions) - negative emotions (insecure), and so forth, but would be
less likely to choose the temperament traits, full plan-initiation
- because (illogical), or verbal-expressive - because (shy), etc.
In the interests of simplifying the task of specifying these action
personality types, a concession has been made (but see
section 4.5.2 for other, more complex, approaches
to this): temperament traits are selected across the entire range of
positively valenced or negatively valenced emotions (i.e., in the second
dimension of the action response space). For example, if an agent is
represented as tending to be repressive with respect to anger, then the agent is also represented as being repressive
with respect to the other negative emotions such as reproach,
remorse, and so forth. Clearly there are personalities which we might
wish to represent which are excluded by such an arrangement. On the other
hand it is easy to imagine that a shy person is shy with respect to
expressions of all of his positive emotions, that a cold person tends to
deny the feeling of any positive or negative emotions, and so forth.
In many cases the expressive temperament traits chosen (or
emphasized) will be the same for both the positively and negatively valenced
emotions. In other words, if one is open in his expressions of
negatively valenced emotions he will also tend to be open with
respect to his positively valenced emotions. However, activation of traits
for the positive emotions and for the negative emotions has been separated
in the Affective Reasoner to give more flexibility in the specification of
personalities, and, specifically, for the representation of moods
(for a discussion of which see section 4.5.2 below).
It should be understood that the decision to force the selection of
temperament traits to be the same for all twelve emotions in each of the two
sets of emotions (i.e., the positive set and the negative set) is not
a restriction imposed by the architecture of the system. At the time the
personality of an agent is created, or his mood altered, each temperament
trait enabled or disabled is ultimately selected, by name, in the rete-like
structure representing the action selection database for each emotion.
(see section 4.5.2 for a discussion of this).
The problem with doing this manually, however, is that one must then specify
each of the 480 temperament trait/emotion duples when defining a
personality. This has not, so far, proven to be worth the effort.
One must be careful not to confuse a predisposition to have
certain emotions with a predisposition to express the emotions that one
has. For example, to describe someone as being an angry person only
suggests that he is likely to experience the anger emotions, a matter
for the construal process to address. No claim is made about this agent's
tendency to express the anger emotions in a retaliatory fashion, to have
somatic responses, and so forth. In addition, no claim is made about the
nature of an agent's expressions of the positively valenced emotions.
Once the system template has been created representing the full
spectrum of potential emotion expression paths it must be personalized
for each agent. This entails copying the entire database and then activating
temperament traits according to the action personality profiles of the
individual agents. Figure 4.3 illustrates this process. The
emotion types, temperament traits, and so forth are combined to form the
system template database. This is copied in full for each new agent
in the system.
Finally, at setup time, or whenever an agent's mood
or manifestative personality is changed, the temperament traits representing
the desired response action profile for that agent are activated.
Figure 4.3:
Creation of personalized action database.
 |
4.4 The functional spectrum of response categories
Because of the complexity of
relationship between emotions and
action responses, we have
implemented a constraint on the range of action profiles represented
wherein at most one token from each action category (i.e., somatic,
repressive, etc.) is allowed to participate in the action set expressing
any given instance of an emotion.The justification is this: our initial purpose is to create interesting,
realistic personalities. To reason effectively about their actions we need
to have some understanding of the structure of their responses to events. It
is sufficient, at least for the time being, to be able to represent a
significant subset of all possible action personalities. Excluding some
personalities from representation in the Affective Reasoner is not
desirable, but neither is it prohibitive for most applications. (The
converse of course, including personalities that one could never encounter,
would be a much greater problem.) The following list gives some of the
benefits of this approach.
- Limiting cases. While there are still a great many ways
of expressing each emotion category, this technique places some limits on
the total number of cases likely to occur, and organizes the cases so that
they will be more easily represented by prototypes.
- Consistency. By organizing the token choices in each
category, some consistency problems can be avoided. For example, some tokens
are not compatible (e.g., induce sympathy and induce fear). This tends to
fall out from the idea that expressions in each action category are purposeful in nature, and that a single expression will serve that
category's purpose; alternate expressions are thus not necessary.
- Intensity spectrum. As discussed above, even though not
used in the present system, the tokens are arranged in a partial
ordering by intensity. Selecting one token suggests the selection of
one intensity. If, in the future, the system is altered to allow more than
one token from each category, then equivalence classes would need to be
created for equal intensity-valued tokens and multiples limited
to tokens from a single class.
- Interesting action sets. This is a very coarse-grained
system with respect to the subtleties of human emotional expression. Given
the limited representational resources available, we must use the relatively
few tokens to their greatest expressive value. Spreading expressive purpose
over different expressive categories helps to achieve this.
The only apparent problem is that some valid action sets may not be
represented. For example, glancing downward and having head bent
(both communicative, non-verbal actions) are obviously compatible but
they will not appear together in this system in its current configuration.
Figure 4.4 illustrates how this constraint works. Action
tokens may be selected from one or more of the action categories. In set (1)
a token has been chosen from each of the three listed categories producing
the action set, blush, have self-directed attributions of unworthiness,
induce sympathy. In set (2) tokens have been selected from two of the three
listed categories producing, have self-directed attributions of
inferiority, and induce fear. Set (3) is not possible in
the Affective Reasoner because it
includes both have self-directed attributions of unworthiness and have self-directed attributions of passiveness, each of which is in the
evaluative category. Set (4) is not possible because it includes induce sympathy and induce fear, each of which is in the other-directed emotion modulation category.
Figure 4.4:
Selecting a single action from each response category.
 |
In this section we discuss the format of the databases comprising
the action component of agents' rudimentary personalities. We have already
discussed the three-dimensional structure of the database and the way the
basic template is copied for each new agent created. We now look in greater
detail at the actual database entries themselves. For this discussion we
focus on a single entry, the communicative, non verbal entry for a
single emotion type, shame. To put this in perspective there are
approximately 480 such entries in an agent's full response-action database.
First we look at the basic format of the entry, explaining each of
its clauses. Next we instantiate each of these clauses in sequence with the
characteristics of shame. Following this we look at a conceptual
view of the configured entry. Lastly, we discuss how the final form of this
shame entry is tuned to represent the individual action response
characteristics of agents in the system.
The basic layout of an action database entry is shown in
figure 4.5. Here we see a set of clauses that
will first be instantiated to represent one of the action response
categories for one of the emotions (e.g., communicative, nonverbal),
will then be copied into a new agent's structure representation,
and then will either be activated or left dormant when the agent's action
personality component is tuned. During action generation for the agent the
processing mechanism reads these clauses from the top down, collecting
bindings as it goes. In this processing, should any of the clauses 3 - 7
fail to have been activated by the addition of working memory, action
generation fails for this temperament trait as an expression of the parent
emotion.
Figure 4.5:
Basic Format of an Action database entry.
 |
Annotations for figure
4.5
- 1 Action template. This will be the output from processing
this database entry. Each time this database entry is used for producing
actions a copy of this template (i.e., (action ?action-set
?bindings)) is instantiated with the final action set selection and the
current bindings. The instantiated template is placed in a temporary queue,
along with other actions selected for other emotion-type /
action-character-trait duples for discrimination by, first the conflict
resolution mechanism, and then the action-generation calling function.
- 2 Index key. This key indexes
the action database entry. Hooks have been left for future improvements to
the current, crude, indexing scheme. When looking for responses to the shame emotions, for example, this database entry will be searched.
- 3 Trait selection clause This clause activates the database
entry for an agent when a matching working memory token is added to the
agent's action database. A discussion of the importance of this clause
follows in section 4.5.2.
- 4 Name of database entry. This is the name of the
emotion-type / temperament trait duple that this database entry represents.
?acts will be bound to the eligible actions for this duple when
working memory is updated during the initialization process. This is a persistent match clause, so called because once it has been altered by a
change to working memory, that change persists until it is further updated
or actively removed.
- 5-7 Optional additional persistent match clauses. Any number
of special working-memory clauses may be added here. Each of these clauses
is again a persistent match clause. The special-wmA token is
simply an identifying constant; the ?special1, ?special2,... variables
are additional parts of the template, and will cause bindings to be stored
when matches are made. For example we might include the clause,
(nil physical-state ?what ?valence ?intensity)
In this case, clauses such as (physical-state head negative severe)
will match when added to the database. The bindings ((?what / head)
(?valence / negative) (?intensity / severe)) will be stored for future
reference. In general, any match configuration is allowed, including any
number of variables. Each time working memory that matches this clause is
added, a new variable binding will be stored for each of the variables. As
with the ?acts variable, these working memory bindings are stored in
place of the nil list entry at the beginning of the clause. In all
cases these bindings are cumulative, so that if more working memory elements
are added that match these clauses, more bindings are stored. Working
memory may only be explicitly removed by working memory identifier.
- 8 Threshold predicate. If used, it is a function that evaluates
to true or nil. If a true value is returned the matching
process continues and an action is selected. If a nil value is
returned processing ceases and no actions are returned for this database
entry.
- 9-11 Additional variable binding functions. Sometimes it is
useful to add additional bindings to the bindings list. These clauses allow
the researcher to list the additional variables and record the functions
that determine their values.
- 12 Temperament trait action selection. The ?act variable
is bound to the final selection of actions for this database entry. ?acts, which contains the candidate actions, must be passed, along
with any other variables needed by the choosing function. In the degenerate
case this function simply makes a random choice from the list of candidates.
2 The trait-selection clause
As stated above, this is a powerful and important clause in the
database. With the current version of the action theory in the Affective
Reasoner, this clause sees only basic use, but the design of the system
allows a much more powerful theory to be expressed. As an example of the
simplest form of its use, suppose the following directive were to be issued
to the simulator:
(add-wm-element '(has-trait negative communicative-non-verbal)
(name-to-action-database 'Tom))
This would cause working memory to be updated for Tom's
action database, specifically the communicative-non-verbal entries for
each of the twelve negative emotion types. Note that the empty
bindings list will be saved since there are no variables in the match
template for this clause. However, the nature of the propagation path
through the action database hierarchy is such that some binding set is
necessary for each clause, even if it is empty, for the path to succeed.
If more than one binding set exists then more than one path through the
hierarchy succeeds.
A number of interesting features are present in this part of
the action database entry. First, it should be obvious that this is simply a
binary control switch to activate and deactivate a temperament trait for an
agent as per the current action theory. However, this need not necessarily
be binary. One might specify instead that actions from a given trait were
to become more likely or less likely to be selected. For
example, the following assertions:
(has-trait negative communicative-non-verbal very-likely)
and
(has-trait negative communicative-non-verbal possibly)
might be inserted in the database matching the the template:
(has-trait negative communicative-non-verbal ?likelihood)
In this case ?likelihood is bound and then passed to
some later function (e.g., the threshold predicate of clause 8) for
evaluation. Similarly we might also record ?arousal-level and ?attentional-focus-level, and so forth, here for the agent, thus affecting
which action is actually selected from this database entry, instead of
merely whether an action is selected. In other words, precisely the
manner in which this trait is activated determines its action selection
characteristics.
Second, note that the token negative is included in
the trait-activation template. As discussed, this is a way of tuning
personalities differently for the negative emotion types and positive
emotion types. It also provides a crude method of allowing an agent's moods to be tuned. As an example of the first case consider the attempt
to create an action personality for an agent, Tom, who never has
anything nice to say. In this case we would issue the following directive:
(add-wm-element
'(has-trait negative communicative-verbal)
(name-to-action-database 'Tom))
which would allow us to select verbal communications as
an expression of the negative emotions. We would explicitly NOT issue
the following, however:
(add-wm-element
'(has-trait positive communicative-verbal)
(name-to-action-database 'Tom))
By not doing so we prohibit verbal communications as expressions of the
positive emotions, which is what we wanted. Note that we are not
representing someone who always sees the world in a bad light, which is
strictly a matter for the interpretive personality component to address.
Instead, we are only tuning the expressions of affective states which
already exist.
With respect to moods, one can imagine that a generally negative
mood might tend to activate certain temperament traits in the negative
emotions and certain other traits in the positive emotions. For example we
might wish to issue the following mood-tuning directives:
(add-wm-element
'(has-trait negative somatic)
(name-to-action-database 'Tom))
and
(remove-wm-element
'(has-trait positive communicative-verbal)
(name-to-action-database 'Tom))
for someone who grows sullen when they are in a bad mood (i.e., to
increase the likelihood of a somatic response for a negative
emotion, and remove the possibility of a verbal-communicative
response for a positive emotion).
Nor are we limited to such a two-dimensional treatment of
moods and personalities. Obviously we can, in principle, represent any
grouping relating temperament traits to moods and personalities within the
emotion space. As an illustration, we might focus one set of working-memory
updates on the standards-based emotions, allowing us, for example, to
specify that an agent will tend to initiate plans as a response to admiration, reproach, gratitude, anger, pride and so forth while not making
any specification for responses to distress, joy, pity and the like.
Such an update might be achieved by the following:
(add-wm-element
'(has-trait standards-based full-plan-initiation)
(name-to-action-database 'Tom))
matching:
(nil has-trait standards-based full-plan-initiation)
but not
(nil has-trait goal-based full-plan-initiation)
We can also combine these two (or more) dimensions giving us such clauses
as:
(add-wm-element
'(has-trait standards-based positive full-plan-initiation)
(name-to-action-database 'Tom))
matching:
(nil has-trait standards-based positive full-plan-initiation)
but not
(nil has-trait goal-based positive full-plan-initiation)
or
(nil has-trait standards-based negative full-plan-initiation)
Lastly we raise the possibility of the grouping of temperament traits
strictly by mood. Just as we use the temperament traits to define the
overall individual characteristics of an agent's action personality, so
might it also be useful to characterize moods the same way.
As a coda to this section we note that, as previously stated, action
working memory in the Affective Reasoner is cumulative. This means that if
some clause is added to working memory more than once, a new path through
the affected database entries may be found for each additional working
memory element. This opens up possibilities for interesting intensity,
duration and mood calculations based on independent forms of input. For
example, some process 
in the simulation may cause a working-memory
item to be added to Tom's action database. Another process 
,
not coordinated with , adds the same working memory item. A duplicate
path through the action database entries is created. This means that
particular tokens are twice as likely to be selected as before to express
some particular affective state.This can be useful for representing the cumulative effects of certain kinds
of interaction between the agents and their environment without having to
control the interaction of asynchronous events.
For example, suppose that the user has set up the simulation so that
at 5:00 P.M., all the agents become more belligerent. Included in this is
an increased likelihood of a verbal expressive response for the
negative emotions. In the hours preceding this 5:00 P.M. global mood change
Tom, an agent, has had a series of goals blocked which, independently, puts
him in a bad mood. This also enables the verbal expressive response
path for the negative emotions in Tom. Tom now has two paths to verbal
expressive responses (or three if he had one enabled before the two
changes just mentioned). This increases the likelihood that Tom will have
one of these emotion manifestations in the output response action set for
any of the negative emotions. No coordination between prior manifestative
personality settings, global mood changes and local mood changes is
necessary.
3 The conceptual view of an instantiated database entry
Figure 4.6 illustrates the
conceptual layout of an instantiated action database entry for shame
as it would appear in the system template database.In other words, this is
a piece of the profile of an action database entry for all agents, before
any personalized tuning has been performed. The filling in of this template
comes in three phases.
The first phase is the creation of the static portion of this
system-wide database. This includes the creation of each of the twenty-four
emotion types (e.g., joy, shame, ...), the approximately twenty
character-trait response categories for each of those emotion types (e.g.,
joy-somatic, joy-behavioral-towards-animate, ... shame-somatic,
shame-behavioral-towards-animate, ...), threshold predicate functions for
each emotion-type/character-trait duple (to determine if a given situation
and emotion actually merits a response), intermediate variable binding
functions (for adding to the bindings list) and a final action-selection
function for selecting one (although possibly more) actions to express each
emotion-type / character-trait duple (e.g., a function to choose between
head-bent and eyes-cast-downward as an expression of shame-communicative-non-verbal).
Figure 4.6:
Conceptual layout of an action database entry for shame,
communicative (non verbal).
 |
The second phase is the addition of system working memory to the
existing static template. This should also be considered as functionally
static since working memory is altered before the agents are created, and
since these working memory elements are not removed.In this stage the action tokens (and templates and mini-plans) for each of
the emotion-type/character-trait duples are added to the database and stored
in each of the associated 480 ?acts variables. For example, this is
where the ?acts variable for shame-communicative-verbal is bound
to mutter, apologize, etc..
The third phase of preparing the action database is where the true
dynamic working memory is updated. When a new agent is created for a
simulation run, the system-wide database produced in the first two phases is
copied whole into this agent's structure representation. Two sets of working
memory modifications are performed on this copy to create the individual
agent's action personality.
(1) the action personality keys are used to enable specific paths
through the action hierarchy. If an agent, Tom, has the temperament
trait somatic, for example, then the statement,
(add-wm-element
'(has-trait somatic)
(name-to-action-database 'Tom))
would be issued enabling somatic action responses to be selected for
the agent Tom. (2) the special persistent match clauses
are tuned. As stated above, these are the optional clauses where additional
working memory items can be stored. Given a physical state clause, we might,
for example, specify that Tom is feeling sick, or even that he is
asleep.
To illustrate how these three phases work we will now build our
example database entry for shame. The following list of annotations
shows how this is done.
Annotations for figure
4.6
- Line 3: Temperament trait activation clause.
As discussed this clause stores an empty binding when the
temperament trait has been activated.
- Line 4: Identifier for temperament trait database entry.
After issuing the following working memory update directive:
(add-wm-element
'(shame-communicative-non-verbal (head-bent glance-downward))
(name-to-action-database 'Tom))
we see that the ?acts variable has been replaced with
two action tokens, head-bent and glance-downward. It is
from this set of two tokens that we will select an appropriate action
for the communicative-non-verbal temperament trait expression path
for the shame emotion. The clause was added in phase one,
and the action tokens were added in phase two.
- Line 5: Optional persistent memory clause. Here we see that an
additional working memory clause has been added. This particular clause
allows us to keep track of the current physical state (or states) of the
agent. In this case working memory has been added so that hungry and
very are bound to the variables ?phys-state and phys-intensity, the effects of making a (phase three) assertion
such as:
(add-wm-element '(physical-state (hungry) (very)))
In other words, the clause was added to the database in phase one,
and the tokens representing the state of Tom being very
hungry were added in phase three. Note that a typical use of these
additional persistent-match clauses is to simultaneously alter a number of
database entries. One can imagine that significant changes in the physical
state, such as being hungry, will affect the expression of a number of
different emotions at once.
- Line 6: Threshold function.
Next we add a threshold predicate which, in our example, says that
if the agent is more than a little bit (medium) hungry, then the
predicate will fail and he will not act to express shame in a communicative-non-verbal way. A projected use of the threshold predicate is
for intensity reasoning (i.e., to determine thresholds under which no action
is taken to express the emotion), although as seen here, it can be used for
any sort of filtering. The threshold predicate may, of course, contain
conjunctive and disjunctive reasoning and so may actually be a composite of
many different tests. This threshold function is added to the database
in phase one and, since it has no working memory component, is not
altered in later phases.
- Line 7: Additional variable binding functions
Next we add some functions for adding bindings to the bindings list.
These are evaluated in series, so the result of one binding may be used in
the determination of another binding. Obviously these bindings could be
performed all at once within a single function, but since one of the stated
functions of the Affective Reasoner is to do psychological modeling and
simulation it was clearly a better choice to make the creation of each
binding as explicit as possible. For example, here we first bind ?duration to some value using the time of the original situation (this is
in the set of bindings passed from the original situation match occurring
during the construal process), the physical state of the agent and the
intensity of that physical state. Next we bind ?intensity-curve to a
value using all of the previous arguments with the addition of the ?duration variable.
To understand the structure of these functions (and the
temperament trait action selection function which follows) consider
how they are evaluated. First, the bindings are used to instantiate
the function arguments. In this case it means that ?time-of-event,
?phys-state, ?phys-intensity and later, ?duration are bound
to values from prior processing of the event and its construal. The
function is then called with these values as arguments.
- Line 8: Temperament trait action-selection function
Lastly we add a function for selecting which of the actions will
represent this temperament trait category (in this case the shame-communicative-non-verbal category). As explained above in
section 4.4, in most cases only a single
action token is selected. This is arbitrary however, and is under the
control of this function. If intensity values are to be used, their
influence will come into play in this function. Using the simplest
intensity algorithm, for example, we would have intensity range from 1 to
and the action tokens ordered by intensity from 1 to . An intensity
of n would cause action token n to be selected. As discussed in
section 4.2 however, it is not
generally possible to fully order the action tokens by intensity within a
temperament trait category for some emotion. Neither is it a simple matter to
construct a meaningful intensity function within a domain.
Alternatively, we might wish to select more than one action for
this emotion / temperament trait path. Additional theory is needed to
specify relationships such as often co-existing and so forth
with respect to relationships between these actions. It should be
understood also that this complicates the task of reasoning about
observed cases of action expressions as well.
Should it happen that the selection criteria for a given set of
response actions does not give precedence to one action over another within
a given response category, an action is chosen at random. Until we have a
better understanding of the relationship between emotions and actions this
randomness of action choice is the best we can do. How can we say what it
is that determines that we shake our head as a non-verbal expression of
reproach in one instance, and throw up our arms in another? On the other
hand, it seems likely that if one action, such as smiling, serves
more than one function (i.e., behavioral-toward-animate,
non-verbal-communicative, other-directed-emotion-modulation, etc.) in the
action response array its likelihood of selection will be increased. The
Affective Reasoner's current selection algorithm supports this.
Finally, we must consider the problem of selecting actions when one
is experiencing mixed emotions. If shouting is expressive of both fear and joy, then it seems likely one might well shout when
experiencing both emotions simultaneously.At present this problem is left open, and actions from each of the emotions
are simply combined in the action event.
4.6 Action conflict sets and exclusion sets
Once a set of actions has been selected in response to an emotion
there is one more step required, that of removing incompatible actions from
the action set or of choosing between competing ``factions'' of sets of
incompatible actions. Restricting each category of action tokens to one
representative eliminates a great many conflicts, but conflicts can still
occur. Manifestations of emotions that are typical for an expressive,
outgoing person tend to conflict with those of a withdrawn, solitary person,
and so forth. To address this problem in the Affective Reasoner, we have
taken a two-step approach. The first step is to group the action tokens
under consideration into named equivalence classes. Figure 4.7
shows portions of three such equivalence classes for the gloating
response: opposite-valenced responses, active/expressive
responses, and withdrawn responses. The second step is to identify
conflicts between the sets such that a member of one set may not co-occur in
the final action set with a member of the second set. In the example we see
that the set of tokens in the equivalence class for active/expressive
responses is not compatible with the set of tokens in the equivalence class
for withdrawn responses. Thus none of the tokens in the first
equivalence class may appear in an output action set with any of the tokens
in the second equivalence class. The sets of tokens in these equivalence
classes are known as action exclusion sets. In this example we have
captured the idea that a person may deny being happy about the misfortune of
someone else and at the same time may exhibit compassion (although not
necessarily feel anything like compassion), but the person cannot do either
of these while also attempting to overtly embarrass the victim.
Figure 4.7:
Action exclusion sets
 |
A token may appear in more than one action exclusion set. For
example, the action token smile may appear only as a suppression manifestation of the emotion distress. As such it would
naturally appear in an action exclusion set for suppressive/distraction-effecting tokens, to be contrasted with the more
direct communicative responses. However, it might also appear in an
action exclusion set for its active quality and would be excluded from
appearing with the tokens in the withdrawn action exclusion set.
(E.g., people tend not to laugh when they are being abnormally quiet).
Exclusion sets are created ad hoc to restrict output response action
sets to viable candidates for each of the emotion types. Here is an example
representation of exclusions sets for distress:
(table-value 'distress *exclusions*)
(make-exclusion
:sets
'((WITHDRAW tired be-quiet low-energy withdraw quiet-voice)
(ACTIVE hyperactive be-active be-entertained smile laugh)
(SUPPRESS laugh smile be-active be-entertained)
(EXPRESS cry drooping-posture frown withdraw))
:incompatible '((WITHDRAW ACTIVE) (SUPRESS EXPRESS)))
The use of action exclusion sets is only one of the ways that the Affective
Reasoner has to make decisions about the final version of the output action
set. As discussed in section 4.5.3, processing may also
be fine-tuned for each of the emotion categories through the use of the
action selection function. Clearly, the resolution of many conflicts is
dependent upon the context in which the action is performed. In general,
this is a difficult problem, and it is of necessity only cursorily
addresssed in the Affective Reasoner.
In this chapter we examined the generation of emotion-based actions
for the agents in the Affective Reasoner. The spectrum of action responses
to emotion eliciting situations has been reduced to approximately twenty
action response categories. Each category groups together responses
that share functional qualities. These categories, together with the
twenty-four emotions create a grid within which the actions themselves are
placed. Actions consist of simple tokens, templates containing variables,
and simple mini-plans. There are roughly 1400 such actions in the system.
The structure resulting from this description of emotion-based action is
used as template for the action databases of the individual agents.
Agents have a manifestative personality component which
controls which temperament traits are active for each of the agents.
Each temperament trait is associated with one of the action response
categories and activates a set of paths through either the set of positively
valenced emotions or through the set of negatively valenced emotions. When
a temperament trait is active for some agent he may manifest actions
associated with the corresponding action response category. The set of
potential response actions generated is reduced so that the actions do not
conflict with one another.
The capabilities of the expressive system beyond the current state of
the action theory were discussed. Actions can be ordered by intensity.
Working memory can be used to capture moods. An increased likelihood
of a specific action being selected to expression an emotion may be
effected. Relationships more complex than simple negative and positive
valence may be established between the activation of temperament
traits and the emotions types.
5 Conclusion
A major goal in developing the Affective Reasoner was to build a
platform for modeling the interactions between multiple agents operating in
an environment in which some of their goals are shared, and others are
unique. The uniqueness of their goals was achieved by endowing each agent
with a rudimentary ``personality'', both with respect to their motives,
standards and preferences, and with respect to certain behavioral
characteristics. A second important goal was to construct a system for
reasoning about the emotions and emotion-induced actions of these agents.
Both of these goals required the design and development of an
emotion-representation scheme, realized in the Affective Reasoner through
data structures as construal frames, GSPs, COOs, temperament trait
propagation networks and so forth.
A third goal was to design the Affective Reasoner in a manner at
least congenial to psychological theory, where such theory is relevant. To
this end, we drew upon a psychological theory of the elicitation of emotions
([18]) which characterizes distinct emotions in terms of the ways
in which agents construe situations, and have expanded a preliminary effort
of Gilboa and Ortony, which attempts to impose some structure on
emotion-based action
[13].
With this machinery, we have been able to make a start not only on
the problem of how to model the emotional reactions that agents might have
to situations that they encounter in their ``world'', but also on how such
reactions might be manifested in behavior, with concomitant consequences in
that world. The system was designed so as to allow agents to build
representations of other agents, representations we called Concerns-of-others databases. These representations are both based on,
and contribute to a capacity to reason about one another's concerns,
reasoning that is prompted by one agent observing the ``behavior'' of
another. In this sense the modeled world contains not only the static
features of the vicinity in which the agents are operating, but also
representations of the agents themselves, including their dynamically
updated representations of others.
In implementing a theory as a computer program, many details have to
be addressed which may result in the theory becoming specified more
precisely. An example of this in the Affective Reasoner is the specification
of the emotion elicitation rules. In addition, it was found necessary to add
two new emotion types. On the other hand pragmatic constraints of building a
running system sometimes necessitate that issues important from a
theoretical perspective be finessed. So, in the Affective Reasoner a number
of simplifying assumptions had to be made in order to complete the cycle of
situation interpretation, emotion generation, response-action generation,
and reasoning about such emotion episodes from the point of view of an
observer. An ideal computer model of this cycle would certainly include,
for example, considerations of emotion intensity and duration, but it simply
was not practical to deal with such issues in addition to those already
being implemented.
To reason effectively about both the situations that lead to
emotions and the actions that derive from them we have adopted two different
approaches. The strong-theory aspects of emotion elicitation suggested the
rule-based approach we used for mapping the features of modeled
situations into emotion representations. The weak-theory aspects of
emotional expression prompted us to incorporate a case-based reasoning
system and to create a simple theory of the structure of emotion
manifestations within the modeled world.
This research is as much an effort in Cognitive Science as it is in
Artificial Intelligence, which suggests two ways in which it might be of
use. First, if the design and implementation of the Affective Reasoner is
sufficiently flexible, it might serve as a helpful vehicle for comparing the
implications of different theories pertaining to emotion elicitation,
emotion-induced action, and the relation between idiosyncratic cognitive and
behavioral characteristics of agents on the one hand and their affective
lives on the other. Second, it may be that elements of this work can
contribute to other areas of AI in which some form of emotion reasoning is
important.
This document was generated using the
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Computer Based Learning Unit, University of Leeds.
Copyright © 1997, 1998, 1999,
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Mathematics Department, Macquarie University, Sydney.
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Footnotes
- ... agents
- TaxiWorld has been run with up to forty agents at a time.
- ...
well.
-
Harry ``knows'' Tom only in the sense that in a system with a limited number
of situation types and interpretations of those situation types, he knows
which interpretations Tom will probably make.
- ... prototype.
-
This simple ``opportunity'' has the form: the other cab driver has right to
the passenger; the other driver may have a negative emotion about this; self has positive emotion about taking passenger; so make a proposal. The
Affective Reasoner allows the user to attach an opportunity-detection
mechanism to the construal frames used to interpret emotion eliciting
situations. These mechanisms are used to recognize situations which might,
if the proper action is taken, lead to other situations in which the
construal frames apply. This simple mechanism allows us to initiate
rudimentary negotiations between agents, which in themselves often give rise
to emotions. Since this is not a sophisticated mechanism, and is not central
to this research, it will not be discussed further.
- ... structure.
-
But see chapter 3 for a discussion of this.
- ... tickets.
-
We can, for example, imagine a situation in which Harry has made a
bet that his friend Tom will not get a speeding ticket. In this case,
if Tom does, in fact, get a speeding ticket then Harry might be
pleased for Tom but displeased for himself.
- ... emotions.
-
Ortony et al. claim that these multiple construals are not, and cannot
be, simultaneous, but rather that agents oscillate between them. The
Affective Reasoner does not make this distinction, although such
oscillation could be represented.
- ... cells.
-
This intensity ordering is not actually being used in the current version
of the Affective Reasoner.
- ...
situation
-
There is no sophistication in such feature selection. Names and roles of the
involved agents are included, and the type of event which has taken place.
Obviously a more sophisticated feature selection algorithm would
allow for more structure in the case base. While quite interesting, this is
not central to the current work.
- ... modes.
-
Actually, the Affective Reasoner also has been used to reason in two
additional modes, which involve empirical learning. Since the development of
these two modes is still in a preliminary stage it will not be discussed
further.
- ... agent.
- Obviously, to make
inferences about why an agent is in some emotional state we must first
know what that state is. This is of course a problem in its own right, and
was discussed in section 2.7.
- ... situation
-
All eliciting situations are typed. Construal frames which interpret them
have the same type. See section 2.2
- ... unit.
-
These three relationships have a very
specific meaning here. Friendship means that an agent will tend to
have similarly valenced emotions in response to the emotions of another
agent. Animosity means that the emotions will tend to be oppositely
valenced. Empathetic unit, the most controversial of the three,
means that the particular situation is seen ``through the eyes'' of
the other agent, so that the emotions are experienced as the
agent's own.
- ...
state.
- Although this is not the point, these anecdotes, where
the feelings of the other agent are not in accord with the known facts, and
yet where the observing agent responds to those feelings, are somewhat hard
to come by. In general this is because if the observing agent knows that the
observed agent will soon find out the facts, the observing agent is much
less likely to base his emotions on the temporary happiness or unhappiness
of the other agent. This adds an element of secrecy, or of quirky twists of
fate (e.g., someone dying before they find out) which in themselves almost
always complicate the situation and the resulting emotions. There seems to
be some difference between the negatively and positively valenced emotions
with respect to this as well. One is more likely to be sad that a friend is
temporarily unhappy because he misunderstands a situation that will
ultimately make him happy, than one is to be happy that a friend is
temporarily happy because he misunderstands a situation that will soon make
him sad.
- ...
clock.
-
This frame has been simplified from that used in the actual simulation of
this episode in the system. Many additional considerations have to be taken
into account.
- ... below).
-
Consider, for example, the case of the taxi driver who is expecting
to get no tip at all because he has offended his passenger. When he gets a
fifteen percent tip after all, he will be happy (relieved). In this case
the tip is of interest because of the prior expectation of not getting it.
- ... wins.
-
This illustration points out how appropriate qualitative reasoning would be
as an extension to this system.
- ...
default
- An example of confirmation by default would be, say, when
we are expecting to not get a phone call from someone we have asked to call
us, and in fact we do not.
- ...
precedence.
-
Actually, the system would probably be wrong to make this interpretation
since it is more likely that in this case the agent would simply feel
admiration and happiness. On the other hand it certainly is plausible that
one might feel gratitude towards a hero who has temporarily given life a
special quality. This is a common occurrence in the lives of performers when
audience members thank them for their work. Further study is needed.
- ... expectations.
- Note that this has not been
implemented for preferences. Positively and negatively valenced construals
of situations seen as objects (i.e., the liking and disliking of objects)
are not usually in negation relationships. In fact, it is the quality of
needing no reason for liking or disliking something that sets
preferences apart from goal-based and attribution-based emotions.
- ... generation.
-
But see
section 2.8 on the structure of the action response with respect
to heuristic classification for a discussion of how these may be used in
abduction, and section 4.5.2 for their use in
specifying personalities and moods.
- ... subjective.
-
Note that it would be difficult to defend any such ordering, even if it were
based on actual studies, since the degree of spontaneity in an action
relative to other actions is meant to at least approximate some functional
attribution, not our assessment of it. The same is true of the intensity
ordering.
- ....
-
In the actual system actions are represented as tokens until after the
exclusion set processing stage (see section 4.6), but, except
for the simple actions, have pointers to the actual templates and plans.
- ... system.
-
The database is copied as a framework for holding agent-specific
information in the form of bindings, as discussed later in this chapter.
However, even this is somewhat misleading. Many elements that are copied are
really only pointers to shared structures. In addition, some design
considerations have had to do with implementing agents so that they
run on different machines and communicate through a server.
- ... emotion.
-
But see item 7 in the annotations
for figure 4.6 for other selection
methods.
- ... removed.
- For the current discussion, the nil
token at the beginning of all the persistent match clauses should be
ignored. They are merely place holders for a list position that will be used
for storage of the currently active bindings for variables in the clause,
and do not participate in the match.
- ... directives:
-
This form is used only for clarity of discussion. Working memory must
actually be removed by explicit working memory identifier.
- ... state.
-
Actually the algorithm is more complex and more interesting than this.
Additional paths through the action database give weight to certain sets of action tokens. This also then affects the likelihood of the set
passing the exclusion set conflict resolution algorithm (discussed in
section 4.6). Also, paths through the database are
multiplicative, so adding two working-memory items, in series, actually
produces one path through the database, adding two sets of two, in
series, produces four paths, two sets of three, six paths, and
so forth.
- ... database.
-
This is conceptual in the sense that it has been simplified and re-ordered
to avoid having to explain uninteresting implementation details.
- ... removed.
-
This was implemented as working memory for the convenience of the variable
binding mechanism, and to leave a hook for large scale dynamic changes of
action context (e.g., from that of being at home to that of being at work)
in future versions of the system.
- ... arguments.
- The
data input macros relieve the researcher from having to
repeatedly enter the variable names however.
- ... simultaneously.
-
These mixed and even conflicting emotions are actually quite common. For
example, one can imagine being happy to have won the regional semi-finals in
the metropolitan opera tryouts, but fearful of having to perform at the
finals. Note that Ortony, et al. ([18]) treat these as oscillating rather than simultaneous.
- ...distress:
-
Some of these tokens are pointers to mini-plans containing
variables and system events.
NORTHWESTERN UNIVERSITY
The Affective Reasoner: A process model of emotions
in a multi-agent system.
A DISSERTATION
SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
for the degree
DOCTOR OF PHILOSOPHY
Field of Computer Science
By
Clark Davidson Elliott
EVANSTON, ILLINOIS
June 1992
© Copyright by Clark Davidson Elliott 1992
All Rights Reserved
Acknowledgments
First and foremost I am indebted to my family for their support through this
long process.
To Professor Lawrence Henschen I am indebted for the careful
guidance he provided from my first days at Northwestern through
graduation, for the strong formal background he gave me in AI,
and for fostering a scholarly atmosphere among his many students.
To Professor Andrew Ortony I am deeply indebted for the faith he has
placed in me as a researcher, for supporting me at the institute, and
for teaching me to be a scholar. In addition I owe him a debt for the
painstaking effort he has given in working with me on this dissertation and
on our collaborative papers.
At the Institute for the Learning Sciences I am indebted to
my professors: Lawrence Birnbaum for continually challenging me to
understand new perspectives, Gregg Collins for his ability to make
clear the most confusing problems in the shortest time, and Chris
Riesbeck for teaching me incantations in LISP and for providing
continual feedback about my work and about the state of AI.
I am also indebted to my colleagues at ILS, among them Eva
Gilboa, my officemate and fellow ``Andrew'' student, Peter Prokopowicz,
Robin Burke, Lucian Hughes and Will Fitzgerald. Unnamed, but not
forgotten, are those ILS students who were here before me and helped
me to learn by example, my classmates with whom I have had so many
long and interesting discussions, and the newer ILS students with
whom I have established ties.
Lastly I am indebted to Roger Schank for bringing together
ILS and populating it with such creative, energetic and committed
people. I cannot imagine a better place to be working on AI than this.
In the Electrical Engineering and Computer Science department I am
indebted to my friends Jung Kim, James Lu, Monica Barback and Hugh Devlin
for their continued support.
I am deeply grateful to my colleagues at DePaul University who first
taught me about AI and who have since supported me in so many ways. In
particular I wish to thank Helmut Epp, who had faith in me from the start,
Gary Andrus who has been a steadfast friend, and Henry Harr who was there at
the beginning. Special thanks are due to Joseph Casey who first started me
on the path to scholarship. Among the others are Hon Wing Cheng, Gerald
Gordon, Marty Kalin, George Knafl, David Miller, Rosalie Nerheim, Ed Pudlo,
Steve Samuels and Jacek Witaszek.
Lastly I want to thank my parents for their support and for always
encouraging me to ask, ``Why?''
To Nell and Peter.
2006-09-11