Journal
ARTIFICIAL LIFE
Volume 11, Issue 1-2, Pages 79-98Publisher
MIT PRESS
DOI: 10.1162/1064546053278991
Keywords
evolutionary robotics; cognition; dynamical systems; homeostasis; learning; development
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We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments, which is an essential aspect of real cognition that is often either bypassed or modeled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion, the origins of learning, and the ontogenetic acquisition of entrainment.
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