@inproceedings{7ba10362adbf4c1fa33eb8ecdf4b464e,

title = "A computational theory of adaptive behavior based on an evolutionary reinforcement mechanism",

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. Track: Artificial Life, Evolutionary Robotics, Adaptive Behavior",

keywords = "Adaptive agents, Adaptive behavior, Conditioned reinforcement, Credit assignment, Delay-reduction theory, Evolutionary algorithms, Matching theory, Reinforcement learning, Rescorla-Wagner rule, Stimulus control",

author = "McDowell, {J. J.} and Soto, {Paul L.} and Jesse Dallery and Saule Kulubekova",

year = "2006",

month = jan,

day = "1",

doi = "10.1145/1143997.1144028",

language = "English (US)",

isbn = "1595931864",

series = "GECCO 2006 - Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery",

pages = "175--182",

booktitle = "GECCO 2006 - Genetic and Evolutionary Computation Conference",

note = "8th Annual Genetic and Evolutionary Computation Conference 2006 ; Conference date: 08-07-2006 Through 12-07-2006",

}