Modeling the Evolution of Motivation

In order for learning to improve the adaptiveness of an animal's behavior and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions in uence its reproductive tness. For example, many circumstances that damage an animal, or otherwise reduce its tness are painful and tend to be avoided. We refer to the mechanism by which an animal evaluates the tness consequences of its actions as a \motivation system," and argue that such a system must evolve along with the behaviors it evaluates. We describe simulations of the evolution of populations of agents instantiating a number of di erent architectures for generating action and learning, in worlds of differing complexity. We nd that in some cases, members of the populations evolve motivation systems that are accurate enough to direct learning so as to increase the tness of the actions the agents perform. Furthermore, the motivation systems tend to incorporate systematic distortions in their representations of the worlds they inhabit; these distortions can increase the adaptiveness of the behavior generated. Department of Cognitive Science; University of California at San Diego; 9600 Gilman Drive; La Jolla, CA 929030515; (619) 534-7308; [email protected] Department of Computer Science and Engineering; University of California at San Diego; 9600 Gilman Drive; La Jolla, CA 92903-0114; (619) 453-4364; [email protected]