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Next: Closing Up: Story-morphing in the Affective Previous: Conclusions

Explanation generation

One of the powerful features of a system like this, which has not been formally utilized, is in its power to generate explanations. In this way stories which might on the surface appear to be similar, can through interaction with the character agents, or through generated narrative, be characterized as stemming from greatly different thematic material.

For example, drawing from the example above, in responding to an interactive query, Elliot himself might explain, ``I was really into racing bikes. I loved the sensations. When I lost I was very ashamed, because it is a very blameworthy thing to be so weak that you always lose at sports and neighborhood activities. It was very important to me to beat Rick. I put a lot of effort into winning. This made it worse. It didn't matter that I only lost once. You either win or lose and that is the end of it.''

Or, the narrator can fill in this information by reporting the same information, but from the third person perspective, ``Elliot was really into racing. He loved the sensations...''

In each case, because the underlying structure of the emotion content is so well understood, and the domain is limited, it should not be an isurmountable problem to use natural language generation techniques to turn the stuctured emotion information, and any extra tagged information (such as why Rick would race bikes even though he did not find the activity attractive), into text which could then be spoken, in real time, by the AR agents (and c.f. the work of Mark Kantrowitz at CMU, and James Lester, et al. at North Carolina State University).



Clark Elliott
Fri Oct 24 15:36:52 EDT 1997