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

8. 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

9. coco

10. kismet

12. 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