Probabilistic Computation and Emotion as Self-regulation
Paper i proceeding, 2015

A treatment of emotion as a means of meta-optimisation in cognitive systems is presented, drawing upon research in neuroscience and reinforcement learning. In particular, emotion is motivated and explained against the background of the free-energy principle and the Bayesian brain hypothesis, from the perspective of appraisal theory. Various implications of these models are examined in the context of reinforcement learning through a review of recent research. Based on the information processing view of computation, a probabilistic approach to modelling computational systems is tentatively proposed in order to better handle the sort of probabilistic information processing involved in modelling cognition. By taking information gain to be the essential property of computation, it is suggested that a general computing system may be modelled as updates of parameters defining probability distributions.

AI

infocomputation

cognitive simulation

Författare

Rickard von Haugwitz

Göteborgs universitet

Gordana Dodig Crnkovic

Chalmers, Tillämpad informationsteknologi, Kognition och kommunikation

Proceedings of the 2015 European Conference on Software Architecture Workshops (ECSAW '15). ACM, New York, NY, USA

a9

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1145/2797433.2797442

ISBN

978-1-4503-3393-1