emotions cse 574 winter 2004

emotions cse 574 winter 2004

affective computing R.W. Picard, Affective Computing limbic / cortical tangle lack of emotion – inefficient decision making (theorem prover run wild?) (Damasio) human/human conventions hold for human/computer interaction (Nass) affective pattern recognition limbic system == inspiration for backpropagation applications teaching environments communication tools entertainment bad faith?

Recognizing Emotions M. Dailey, G. Cottrell, R. Adlophs, “A six-unit network is all you need to discover happiness” Input: 29x36 grid of “wavelets” – transformation of image to a sum of period signals (frequency domain) Principle component analysis to reduce dimensionality Classification by 6-unit neural network Biologically plausible

Purposeful Emotions J.D. Valaqsquez, When robots weep: emotional memories & decision-making, AAAI 1998. Emotions as non-conscious biasing mechanism – “somatic marker” of past experience – functions as alarm or incentive (A. Damasio, Descartes Error) drives – impels agent into action emotion system anger, fear, distress, happiness, disgust, surprise; mixes triggered by releasers can learn associations between stimuli & emotion (e.g. image of pea soup & disgust) behavior system – set of self-interested behaviors (play, approach) triggered/inhibited by drives, emotions, & each other

coco

kismet

Big Picture What are the components of computational theory of human intelligence? What kinds of applications need to consider each? Which are universal to any kind of intelligent organism or artifact? Which are unique to social beings? To human beings? What are appropriate ways to model these phenomena? Are the models psychologically plausible descriptions of How we think? How we think about others? part 1: discourse understanding speech act theory beliefs about beliefs and goal planning utterances interpreting utterances the structure of discourse reinforcement learning in discourse analysis part 2: behavior recognition technical foundations: from Markov models to Dynamic Bayes Nets modeling events with structure and continuous time learning user models modeling user errors and emotions applications part 3: creativity & emotion theories of creativity computers that create art and music emotional computers