Emotion-based Agents: putting the puzzle together

1. Institute for Systems and Robotics Emotion-based Agents:putting the puzzle together Rodrigo Ventura http://www.isr.ist.utl.pt/~yoda email: yoda@isr.ist.utl.pt

2. Emotions versus Rationality • René Descartes, • Discourse on the Method, 1637. • Disembodied mind: reason proper separated from body proper • Emotions and Rationality • Antonio Damásio, • Descartes’ Error, 1994. • Emotion mechanisms take an important role in reasoning processes

3. Research goals: To understand emotion mechanisms, w.r.t. the design of autonomous agents: • Coping with complex and dynamic environments • Capable of determining and using relevance • Intuition: emotions provide a rough assessment of a situation, which can be refined by cognitive processes

4. Approaching emotions from twodifferent perspectives • External manifestations  social interaction • Kismo [Breazeal] • Affective computing [Picard] • Believable agents [Reilly] • HCI [Pelachaud]

5. Approaching emotions from twodifferent perspectives • Internal manifestations  behavioral consequences • “Future myopia” [Damásio] • Alarm system [Sloman] • Appraisal theory [Frijda] • Category of perceptions [Arzi-Gonczarowski]

6. SENSORY CORTEX high road SENSORY THALAMUS AMYGDALA low road emotional stimulus emotional response • Lessons from neurobiology • [LeDoux] high and low roads to the amygdala

7. Lessons from neurobiology • [Damasio] somatic marker hypothesis • the association of certain sensory images with body states • e.g.: experiencing a gut feeling when a certain response option comes into mind, however fleetingly • lesions in the emotional circuitry of the brain lead to “future myopia,”i.e., inability to preview long-term consequences of one’s own actions

8. cognitive image ic(t) stimulus s(t) perceptual image ip(t) • The DARE model: Double representation of stimuli • cognitive image - oriented towards recognition • "what is it?" complex, slow • perceptual image - feature extraction • "what to do?" simple, fast

9. Illustrative example: handwritten digit recognition • binary images, 32x32 pixels stimuli and cognitive images: ic = s perceptual images: ip

10. (Ip, Ic) action Ic(t) matches future future future Movie-in-the-brain • puzzle piece #1: • “Movie-in-the-brain” and the inverted pendulum experiment • stored sequence of frames consisting of(Ic, Ip) pair and ensuing action • the Ic(t) extracted from the present stimulus is matched against the stored sequence

11. The inverted pendulum experiment • Perceptual level: bang-bang tunning • Cognitive level: “movie-in-the-brain” Perceptual level Perceptual + cognitive levels

12. (3) ic+(t) ic(t) ip(t) (2) s(t) memory (1) 1. perceptual metric 2. cognitive metric 3. minimization Sp(t) • puzzle piece #2: • Metric spaces and the handwritten digit recognition experiment • Assume that the spaces of cognitive and perceptual images are metric spaces • Indexing mechanism

13. Theoretical results: • Under certain circumstances, there are garantees that the best cognitive match is found, using the indexing mechanism • Experimental results: • Significative efficiency gain, using the indexing mechanism

14. puzzle piece #3: • Work in progress: finding relevant features • Example: Pavlov conditioning why the bell? • Example: dataset of 2000 handwritten digits, with 649 features each  what are the relevant features for a correct classification? • Dimensionality reduction methods: PCA, NMF, LSA, MDS, etc.

15. Research perspectives • GOAL: To construct a formal/theoretic model of an emotion-based agent • TOOLS: • Relevance • Conditioning • Chunking