title: Evolutionary robotics: a new scientific tool for studying cognition.
abstract: 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.
authors.affiliation: Centre for Computational Neuroscience and Robotics (CCNR), Evolutionary and Adaptive Systems Group (EASy), COGS/Informatics, University of Sussex, Brighton BN1 9QH, UK. inmanh@susx.ac.uk
authors.forename: Inman
authors.initials: I
authors.lastname: Harvey
authors.forename: Ezequiel
authors.initials: E
authors.lastname: Di Paolo
authors.forename: Rachel
authors.initials: R
authors.lastname: Wood
authors.forename: Matt
authors.initials: M
authors.lastname: Quinn
authors.forename: Elio
authors.initials: E
authors.lastname: Tuci
mesh_terms: D000818:Animals; D005075:Biological Evolution; D003071:Cognition / Q000502:physiology*; D006801:Humans; D012371:Robotics / Q000379:methods* / Q000639:trends*