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. University of Central Floridawww.ucf.edu Agenda  Introduction  Highlighted Projects  Affective Cognitive Learning & Decision Making  Questions

3. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making  Affective biases  Two-armed bandit

10. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making  Loss aversion  Prefer avoiding losses than acquiring gains

11. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making  Effect of mood on decision making HAPPINESS Optimism about the present Pessimism about the future FEAR ANGER Optimism about the future Pessimism about the present SADNESS

12. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making

14. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making  The Decision- Making Model  Cognitive state (c)  Affective state (a)  Decision (d)

15. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making  Affective agent vs. Non-affect agent

17. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making  Influence of an outlier on the cognitive values and the valence values

18. University of Central Floridawww.ucf.edu 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 induced inverse temperature parameter decreases  Humans depending on emotion in decision-making

19. University of Central Floridawww.ucf.edu Affective-Cognitive Learning & Decision Making  10-armed bandit tasks

20. University of Central Floridawww.ucf.edu 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. University of Central Floridawww.ucf.edu 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?