Affective Computing: Agents With Emotion

1. Affective Computing: Agents With Emotion Victor C. Hung University of Central Florida – Orlando, FL EEL6938: Special Topics in Autonomous Agents March 29, 2007

2. Agenda • Introduction • Highlighted Projects • Affective Cognitive Learning & Decision Making • Questions

3. Introduction • Affective Computing relates to, arises from, or deliberately influences emotion or other affective phenomena • Engineering, computer science with psychology, cognitive science, neuroscience, sociology, education, psychophysiology, ethics … • Emotion is fundamental to human experience • Cognition • Perception • Learning • Communication • Rational decision-making

4. Introduction • Technologists have largely ignored emotion • Affect has been misunderstood • Hard to measure • MIT Media Lab: Affective Computing • http://affect.media.mit.edu • Develop new technologies and theories • Understanding affect and its role in human experience • Restore a proper balance between emotion and cognition in the design of technologies for addressing human needs

5. Introduction • Issues in affective computing • Communication of affective-cognitive states to machines • Techniques to assess frustration, stress, and mood indirectly • Make computers can be more emotionally intelligent • Personal technologies for improving self-awareness of affective states • Emotion’s influences personal health • Ethics

6. Highlighted Projects • Affective-Cognitive Framework for Machine Learning and Decision-Making • Emotion’s role in learning and decision making • Digital Story Explication as it Relates to Emotional Needs and Learning • Emotional interaction in child learning • ESP - The Emotional-Social Intelligence Prosthesis • Aid for the emotionally-impaired

7. Highlighted Projects • Fostering Affect Awareness and Regulation in Learning • Combat frustration during the learning process • Machine Learning and Pattern Recognition with Multiple Modalities • Emotional sensor data fusion • Ripley: A Conversational Robot • Human-robot interaction platform through language and visual perception modalities

8. Affective-Cognitive Learning & Decision Making • (2006) Ahn and Picard’s “Affective-Cognitive Learning and Decision Making: The Role of Emotions”, The 18th European Meeting on Cybernetics and Systems Research • Framework for learning and decision making • Inspired by neural basis of motivations and the role of emotions in human behavior • Affective biases • Loss aversion • Effect of mood on decision making

9. Affective-Cognitive Learning & Decision Making • Affective biases • Two-armed bandit

10. Affective-Cognitive Learning & Decision Making • Loss aversion • Prefer avoiding losses than acquiring gains

11. Affective-Cognitive Learning & Decision Making • Effect of mood on decision making ANGER Optimism about the future HAPPINESS Optimism about the present Pessimism about the future FEAR Pessimism about the present SADNESS

12. Affective-Cognitive Learning & Decision Making • A motivational value (reward)-based learning theory: • Extrinsic value from the cognitive (deliberative and analytic) systems • Intrinsic value from multiple affective systems such as Seeking (Wanting), Fear, Rage, and other circuits • Probabilistic models • Cognition (cognitive state transition) • Multiple affect circuits (Seeking, Joy, Anger, Fear, ...) • Decision making model • Previous knowledge can be incorporated for expecting the consequences of decisions (or computing the cognitive value)

13. Affective-Cognitive Learning & Decision Making

14. Affective-Cognitive Learning & Decision Making • The Decision-Making Model • Cognitive state (c) • Affective state (a) • Decision (d)

15. Affective-Cognitive Learning & Decision Making • Affective seeking value = • Valence = decided by the mean of the filtered values for the reward samples • Arousal = uncertainty of the reward sample distribution (modeled as standard deviation) • Complete decision-making expression: • Non-affect agent has only the cognitive component

16. Affective-Cognitive Learning & Decision Making • Affective agent vs. Non-affect agent

17. Affective-Cognitive Learning & Decision Making • Influence of an outlier on the cognitive values and the valence values

18. Affective-Cognitive Learning & Decision Making • Affective component less sensitive to outliers than cognitive component • Affective Cooling: Agreement between two components • More likely to follow the decision by the cognitive component (Exploitation) • Value of the induced inverse temperature parameter increases • Humans using cognition in decision-making • Affective Heating: Conflict between two components • Less likely to follow the decision by the cognitive component (Exploration) • Value of the inducedinverse temperature parameter decreases • Humans depending on emotion in decision-making

19. Affective-Cognitive Learning & Decision Making • 10-armed bandit tasks

20. Affective-Cognitive Learning & Decision Making • Too much or too little affect impairs learning • Excessive learns faster, but not good for long-term • Insufficient better for long-term, but slow

21. Affective-Cognitive Learning & Decision Making • Results and Conclusions • Framework enhancements • Model other affect circuits • Incidental influences on decision making • Use of prior knowledge for expecting cognitive outcomes ･ • Affective bias • Helps automatically regulate exploration and exploitation • Speed up learning without sacrificing decision quality • This framework might mimic well-studied human behavior • Risk aversion • Effects of mood on decision making • Self-control

22. Questions?