A demonstration of neural programming applied to non-Markovian problems

Abstract

Genetic programming may be seen as a recent incarnation of a long-held goal in evolutionary computation: to develop actual computational devices through evolutionary search. Genetic programming is particularly attractive because of the generality of its application, but it has rarely been used in environments requiring iteration, recursion, or internal state. In this paper we investigate a version of genetic programming developed originally by Astro Teller called neural programming. Neural programming has a cyclic graph representation which lends itself naturally to implicit internal state and recurrence, but previously has been used primarily for problems which do not need these features. In this paper we show a successful application of neural programming to various partially observable Markov decision processes, originally developed for the learning classifier system community, and which require the use of internal state and iteration. © Springer-Verlag Berlin Heidelberg 2004.