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An Evolutionary Ecological Approach to the Study of Learning Behavior Using a Robot-Based Model

Centre for Computational Neuroscience and Robotics, and School of Cognitive and Computing Sciences, University of Sussex

Matt Quinn

Centre for Computational Neuroscience and Robotics, and School of Cognitive and Computing Sciences, University of Sussex

Inman Harvey

Centre for Computational Neuroscience and Robotics, and School of Cognitive and Computing Sciences, University of Sussex

We are interested in the construction of ecological models of the evolution of learning behavior using methodological tools developed in the field of evolutionary robotics. In this article, we explore the applicability of integrated (i.e., nonmodular) neural networks with fixed connection weights and simple 'leaky-integrator' neurons as controllers for autonomous learning robots. In contrast to Yamauchi and Beer (1994a), we show that such a control system is capable of integrating reactive and learned behaviour without explicitly needing hand-designed modules, dedicated to a particular behavior, or an externally introduced reinforcement signal. In our model, evolutionary and ecological contingencies structure the controller and the behavioral responses of the robot. This allows us to concentrate on examining the conditions under which learning behavior evolves.

Key Words: learning behavior; dynamic neural networks; evolutionary robotics; genetic algorithms

Adaptive Behavior, Vol. 10, No. 3-4, 201-221 (2002)


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