Evolving Improved Opponent Intelligence

Artificially intelligent opponents in commercial computer games are almost exclusively controlled by manuallydesigned scripts. With increasing game complexity, the scripts tend to become quite complex too. As a consequence they often contain “holes” 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 off-line 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. We conclude that evolutionary learning is potentially an effective tool to improve quality of opponent intelligence in commercial computer games.