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The Dynamics of Associative Learning in an Evolved Situated Agent

By Eduardo Izquierdo and Inman Harvey

Abstract

Artificial agents controlled by dynamic recurrent node net- works with fixed weights are evolved to search for food and associate it with one of two different temperatures depending on experience. The task requires either instrumental or classical conditioned responses to be learned. The paper extends previous work in this area by requiring that a situated agent be capable of re-learning during its lifetime. We anal- yse the best-evolved agents behaviour and explain in some depth how it arises from the dynamics of the coupled agent-environment system

Publisher: Springer-Verlag
Year: 2007
OAI identifier: oai:sro.sussex.ac.uk:30478
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