Evolutionary robotics: a new scientific tool for studying cognition.
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.
Harvey
Inman
I
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
Di Paolo
Ezequiel
E
Wood
Rachel
R
Quinn
Matt
M
Tuci
Elio
E
D000818:Animals; D005075:Biological Evolution; D003071:Cognition / Q000502:physiology*; D006801:Humans; D012371:Robotics / Q000379:methods* / Q000639:trends*