Evolution of Mapmaking: 
Learning, planning, and memory using Genetic Programming

David Andre
Stanford University
P.O. Box 5402
Stanford CA 94309
email: phred@leland.stanford.edu

Abstract: An essential component of an intelligent agent is the
ability to observe, encode, and use information about its environment.
Traditional approaches to Genetic Programming have focused on evolving
functional or reactive programs with only a minimal use of state.
This paper presents an approach for investigating the evolution of
learning, planning, and memory using Genetic Programming.  The
approach uses a multi- phasic fitness environment that enforces the
use of memory and allows fairly straightforward comprehension of the
evolved representations .  An illustrative problem of 'gold'
collection is used to demonstrate the usefulness of the approach.  The
results indicate that the approach can evolve programs that store
simple representations of their environments and use these
representations to produce simple plans.