Online adaptation of game opponent AI in simulation and in practice

Abstract

Unsupervised online learning in commercial computer games allows computer-controlled opponents to adapt to the way the game is being played, thereby providing a mechanism to deal with weaknesses in the game AI and to respond to changes in human player tactics. For online learning to work in practice, it must be fast, effective, robust, and efficient. This paper proposes a novel technique called “dynamic scripting” that meets these requirements. In dynamic scripting an adaptive rulebase is used for the generation of intelligent opponents on the fly. The performance of dynamic scripting is evaluated in an experiment in which the adaptive players are pitted against a collective of manually designed tactics in a simulated computer roleplaying game and in a module for the state-of-the-art commercial game NEVERWINTER NIGHTS. The results indicate that dynamic scripting succeeds in endowing computer-controlled opponents with successful adaptive performance. We therefore conclude that dynamic scripting can be successfully applied to the online adaptation of computer game opponent AI.