In our current research, we simulate simple worlds populated with with agents capable of responding ``emotionally'' as a function of their concerns. Agents are given unique pseudo-personalities modeled as both a set of appraisal frames representing their individual goals, principles, preferences, and moods, and as a set of channels for the expression of emotions. Combinations of appraisal frames are used to create agents' interpretations of situations that unfold in the simulation. These interpretations, in turn, can be characterized by the simulator in terms of the eliciting conditions for emotions. As a result, in some cases agents ``have emotions,'' which then may be expressed in ways that are observable by other agents, and as new simulation events which might perturb future situations. Additionally, agents use a case-based heuristic classification system to reason about the emotions other agents are presumed to be having, and to form representations of those other agents' personalities that will help them to predict and explain future emotion episodes involving the observed agent [Elliott and Ortony1992].
Ortony, et al. [Ortony et al. 1988] discuss twenty-two emotion types based on valenced reactions to situations being construed as goal-relevant events, acts of accountable agents, or attractive or unattractive objects (including agents interpreted as objects). This theory has been extended to include the two additional emotion types of love and hate [Elliott1992]. A summary of these emotion types using groupings based on the associated eliciting conditions appears in figure 1.
Previous implementations of the AR allowed the mapping of situations into the twenty-four emotion types by reasoning about simple eliciting conditions, but they did not provide for the determination of emotion intensity. Using the work of Ortony, et al. [Ortony et al. 1988] as a guide, we analyzed a set of descriptions of such situations and created a set of emotion intensity variables to explain the causes of varying emotion intensity, within a coarse-grained simulation paradigm. We reduced the resulting set of variables to a computable formalism, and represented sample situations in the AR. We then isolated three areas of the simulation where variables in either the short-term state of an agent, the long-term disposition of an agent, or the emotion-eliciting situation itself helped to determine the intensity of the agent's subsequent affective state. These three areas, and the intensity-relevant variables embodied in them, are discussed in the next three sections.