From biological models to the evolution of robot control systems.
Attempts to formulate realistic models of the development of the human oculomotor control system have led to the conclusion that evolutionary factors play a crucial role. Moreover, even rather coarse simulations of the biological evolutionary processes result in adaptable control systems that are considerably more efficient than those designed by human researchers. In this paper I shall describe some of the aspects of these biological models that are likely to be useful for building robot control systems. In particular, I shall consider the evolution of appropriate innate starting points for learning/adaptation, patterns of learning rates that vary across different system components, learning rates that vary during the system's lifetime, and the relevance of individual differences across the evolved populations.
Bullinaria
John A
JA
School of Computer Science, The University of Birmingham, Birmingham B15 2TT, UK. j.bullinaria@physics.org
D000222:Adaptation, Physiological / Q000502:physiology; D000375:Aging / Q000502:physiology; D005075:Biological Evolution; D003198:Computer Simulation; D005133:Eye Movements / Q000502:physiology*; D005246:Feedback / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D008957:Models, Genetic; D011157:Population Dynamics; D012371:Robotics / Q000379:methods*; D012641:Selection, Genetic