A computational theory of adaptive behavior based on an evolutionary reinforcement mechanism

Authors:
J. J McDowell;Paul L. Soto;Jesse Dallery;Saule Kulubekova
Affiliations:
Emory University, Atlanta, GA;University of Florida, Gainesville, FL;University of Florida, Gainesville, FL;Emory University, Atlanta, GA
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

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Abstract

Two mathematical and two computational theories from the field of human and animal learning are combined to produce a more general theory of adaptive behavior. The cornerstone of this theory is an evolutionary algorithm for reinforcement learning that instantiates the idea that behavior evolves in response to selection pressure from the environment in the form of reinforcement. The evolutionary reinforcement algorithm, along with its associated equilibrium theory, are combined with a mathematical theory of conditioned reinforcement and a computational theory of associative learning that together solve the problem of credit assignment in a biologically plausible way. The result is a biologically-inspired computational theory that enables an artificial organism to adapt continuously to changing environmental conditions and to generate adaptive state-action sequences.