Improved opponent intelligence trough offline learning
Pieter H.M. Spronck, Ida G. Sprinkhuizen-Kuyper, Eric O. Postma · IJIGS. International journal of intelligent games & simulation · 2003
Artificially intelligent opponents in commercial computer games are almost exclusively controlled by manually designed scripts. With increasing game complexity, the scripts tend to become quite complex too. As a consequence they often contain that can be exploited by the human player. The research question addressed in this paper reads: How can evolutionary learning techniques be applied to improve the quality of opponent intelligence in commercial computer games? We study the offline application of evolutionary learning to generate neural-network controlled opponents for a complex strategy game called PICOVERSE. The results show that the evolved opponents outperform a manually-scripted opponent. In addition, it is shown that evolved opponents are capable of identifying and exploiting holes in a scripted opponent and exhibiting original tactical behaviour. We conclude that evolutionary learning is an effective tool to improve the quality of opponent intelligence in commercial computer games.