Emotion-based Agents: putting the puzzle together Rodrigo Ventura Institute for Systems and Robotics

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

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

Approaching emotions from two different perspectives 1.External manifestations  social interaction Kismo [Breazeal] Affective computing [Picard] Believable agents [Reilly] HCI [Pelachaud]

Approaching emotions from two different perspectives 2.Internal manifestations  behavioral consequences “Future myopia” [Damásio] Alarm system [Sloman] Appraisal theory [Frijda] Category of perceptions [Arzi-Gonczarowski]

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

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

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 stimulus s(t) cognitive image i c (t) perceptual image i p (t)

Illustrative example: handwritten digit recognition –binary images, 32x32 pixels stimuli and cognitive images: i c = s perceptual images: i p

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 Ic(t) matches Movie-in-the-brain future (Ip, Ic) action

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

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 memory i c (t) i p (t) s(t) i c + (t) (1) (2) (3) 1. perceptual metric 2. cognitive metric 3. minimization S p (t)

–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

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.

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