Neuro-evolution in Zero-Sum Perfect Information Games on the Android OS

Iuhasz, Gabriel; Negru, Viorel

doi:10.2478/v10324-012-0013-4

Abstract

In recent years significant work has been done to use Neural Networks in game AI, and harness the advantages of such a technique. This paper would like to show that it is possible using neuroevolution to evolve a neural network topology optimized for a given task and avoiding over or under complexification by human hands. In order to illustrate this we implement two agents capable of playing simple zero sum perfect information games with the help of the genetic algorithm Neuroevolution of augmenting topologies. To illustrate this optimization we load the resulting topologies onto an Android OS game app.

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Neuro-evolution in Zero-Sum Perfect Information Games on the Android OS

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