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An Affective Agent Playing  Tic-Tac-Toe as Part of a Healing Environment ,[object Object]
Overview of this presentation ,[object Object],[object Object],[object Object],[object Object]
Background ,[object Object],[object Object],[object Object],[object Object],[object Object]
Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Background ,[object Object],[object Object]
Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Models Incorporated in the Agent ,[object Object]
The Models Incorporated in the Agent ,[object Object]
The Models Incorporated in the Agent ,[object Object]
Combined model: Silicon Coppélia ,[object Object],[object Object],[object Object]
 
Determining which action to take ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Calculating Hope and Fear ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Calculating Hope and Fear ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Calculating Joy and Distress ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Calculating Surprise ,[object Object],[object Object],[object Object]
Emotion expression by agent ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
Agent 1: The agent tries to win ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agent 2: The agent deliberately tries to lose ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agent 3: The agent decides whether it wants to win based on its money ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agent 4: The agent is too involved with the user to win ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Agent 5: The agent is balanced, and wins sometimes, and loses sometimes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions?

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An Affective Agent Playing Tic-Tac-Toe as Part of a Healing Environment

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Editor's Notes

  1. So: Robot-Animals can help against Loneliness
  2. Animal better than human, because it is acceptable to be a bit awkward.
  3. 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
  4. 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
  5. 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
  6. Give example with [0, 0.4, 0.8]
  7. Surprise  This goes for all agents, but I’m not gonna repeat this