| Summary: | The work described in this thesis concerns the study of the evolution of simple forms of learning behaviour in artificial agents. Our interest in the phylogeny of learning has been developed within the theoretical framework provided by the "ecological approach" to the study of learning. The latter is a recent theoretical and methodological perspective which, contrary to that suggested by the classical approaches in animal and comparative psychology, has reconsidered the importance of the evolutionary analysis of learning as a species- niche-specific adaptive process, which should be investigated by employing the conceptual apparatus originally developed by J. J. Gibson within the context of visual perception. However, it has been acknowledged in the literature that methodological difficulties are hindering the evolutionary ecological study of learning. We argue that methodological tools - i. e., artificial agent based models - recently developed within the context of biologically-oriented cognitive science can potentially represent a complementary methodology to investigate issues concerning the evolutionary history of learning without losing sight of the complexity of the ecological perspective. Thus, the experimental work presented in this thesis contributes to the discussion on the adaptive significance of learning, through the analysis of the evolution of simple forms of associative learning in artificial agents. Part of the work of the thesis focuses on the study of the nature of the selection pressures which facilitate the evolution of associative learning. The results of these simulations suggest that ecological factors might prevent the selection from operating in favour of those elements of the "learning machinery" which, given the varying nature of the environment, are of potential benefit for the agents. Other simulations highlight the properties of the agent control structure and the characteristics of particular features of the ecology of the learning scenario which facilitate the evolution of learning agents
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