Modeling the Evolution of Motivation
John Batali
William Noble Grundy
Evolutionary Computation
4(3):235-270,
1996.
Abstract
In order for learning to improve the adaptiveness of an animal's
behavior, and thus provide any sort of guide to evolution, the
learning mechanism must incorporate an innate evaluation of how the
animal's actions influence its reproductive fitness. For example, many
circumstances that damage an animal, or otherwise reduce its fitness
are painful and tend to be avoided. We refer to the mechanism by which
an animal evaluates the fitness 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 different
architectures for generating action and learning, in worlds of
differing complexity. We find that in some cases, members of the
populations evolve motivation systems that are accurate enough to
direct learning so as to increase the fitness 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 often increase the adaptiveness
of the behavior generated.
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