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emotions

Delve into the world of affective computing, where emotions are recognized and analyzed to enhance decision-making and communication in human-computer interactions. Learn about the impact of emotions on behavior, the role of emotional memories, and the principles behind emotional biasing mechanisms. Discover applications in teaching, environments, communication tools, and entertainment, while questioning the psychological plausibility of computational models. Unveil the components of human intelligence and the relevance of emotions in creativity, art, and music generation.

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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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