An Affective Agent Playing Tic-Tac-Toe as Part of a Healing Environment
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The document describes an affective virtual agent that can play tic-tac-toe and is designed to simulate human-like emotional behavior and decision making. The agent incorporates three models - I-PEFiC, EMA, and CoMERG - to simulate affective decision making, emotions, and emotion regulation strategies. Different configurations of the agent allow it to try to win, try to lose, or make decisions based on its simulated emotional state and the game context. The agent is proposed as a pilot application for an artificial companion that could help reduce loneliness.
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An Affective Agent Playing Tic-Tac-Toe as Part of a Healing Environment
- 26. Questions?
Editor's Notes
- So: Robot-Animals can help against Loneliness
- Animal better than human, because it is acceptable to be a bit awkward.
- Develop state predicates about opponent Appraisal proces: Acquire personal meaning by comparing to personal goals, beliefs, concerns (through relevance / valence) Appraisal feeds into involvement / distance, and leads to (perhaps ambiguous) emotions Affective decision making process, which leads to a new situation
- Expected satisfaction will be high if: High action tendency is generated (belief action facilitates desired goals) Level of positivity action matches biased involvement with the user Level of negativity action matches biased distance with the user Action with highest expected satisfaction is picked
- Likelihood close to ‘f’ (fatalism) increases hope_for_goal High ambition level increases hope_for_goal Negative ambition level Hope for undesired goal state = fear Function is smooth (bell-form), not linear
- Give example with [0, 0.4, 0.8]
- Surprise This goes for all agents, but I’m not gonna repeat this