Therefore, LetterGen is much more dependent in this case on the user to provide focussed content for the acts in the form of input text. It seems possible that focussed situations could be inferred if LetterGen could use its model to parse the user's e-mail. But a history of interaction with the user and with his correspondents is likely to be more helpful. Figure 4 A letter aimed at terminating a cordial relationship 5. An important influence on LetterGen's social model is the work of Schank and Abelson on interpersonal themes .
Themes are rules which spawn goals when triggered by certain situations.
AR's agents are customers and taxi drivers in a simulated TaxiWorld. The agents construe events and react to them in the emotion-laden manner one would expect in such a domain. In both systems, the agents not only model emotional reactions to their own surroundings but also reason about the likely emotional reactions of other agents. The agents represent beliefs, goals, and relationships as these relate to emotions. Yet, the agents interact only with other simulated agents, not with humans and not on behalf of humans.