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The general idea behind the model we are using is that AR agents have
relatively reusable structures for appraising the world. The same structures
that give them their own dispositions, can be built, and maintained, for
other agents as well. The vehicle for attempting to model some rudimentary
form of the affective state of users is based on this idea:
- AR agents have a dispositional component which determines how they
appraise the world. This frame-based structure allows them to interpret
situations that arise in ways that may give rise to emotion responses.
- Because agents have emotions about the fortunes of other agents, it is
necessary for them to also maintain similar structures for these other
agents. In other words, if the experiencing agent's team wins, for
example, he will be happy for himself, but might gloat over an agent rooting
for the other team. To effect this the agent's own appraisal structure must
result in an appraisal of an achieved goal from the situation, but the
agent's own structure of the presumed goals of the second agent must
result in an appraisal of a blocked goal from that same situation.
- Agents, who already keep these concerns-of-others structures, can
maintain them for users as well.
- A perfect structure of each individual user's goals, principles, and
preferences, (e.g., a perfect affective user model, albeit begging the
question of updating it correctly) would allow a great many correct
inferences to be made about their emotion responses to the situations that
arise while using the system. Since this will not be the case, it is
necessary for us to use multiple types of inference:
- Ask the user. In work with the AR, it appears to be true that users
are motivated to express themselves to a computer agent who appears
to have some understanding of how they feel.
- Use other known information to make assumptions about user types.
Some users like to win, some like to have fun, some prefer to follow the
rules, some are impatient. These qualities will tend to remain constant
across tasks and domains.
- Use context information. For example, a user who has just repeatedly
failed is likely to feel bad, whereas one who has been successful is likely
to feel good.
- How would most users feel? The more user models extant, the
stronger a prototype we have for a typical user.
- If all else fails, what would the agent feel if it happened to him?
Agents have affective lives too. One can always ask how they themselves
would feel, and make the assumption that the user would feel that way too\
(i.e., the agent would filter the situation through its own appraisal
mechanism and examine the resulting emotions which do, or do not, arise).
- Although not intended to be part of the preliminary tutoring system
work, AR agents do have access to a case-based reasoning component that
allows them to reason abductively back from actions assumed to be manifested
as the result of an affective state. In this way, agents collect
case information, and are trained, to heuristically classify sets of tokens
present in the environment as representing differing emotion expressions.
Our hypothesis is that we can be at least minimally effective at
filling in missing information when working from a structure that specifies
(a) what is likely to be true of the antecedents of user emotion,
and (b) gives us a high-level understanding of different plausible affective
user models, in a relatively comprehensive (albeit purely descriptive) model
of human emotion. In other words, having a rigorously defined model of what
user affect we are looking for helps us to map system events, and direct
queries of the user, effectively.
Next: Examples of user goals
Up: Affective User Modeling
Previous: Herman the Bug
Clark Elliott
Mon Mar 10 19:53:21 EST 1997