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emotions

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)

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emotions

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  1. emotions cse 574 winter 2004

  2. emotions cse 574 winter 2004

  3. 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?

  4. 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

  5. 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

  6. coco

  7. kismet

  8. Big Picture • 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 • 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?

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