Opponent modelling for case-based adaptive game AI

In previous work we introduced a novel approach to adaptive game AI that was focussed on the rapid and reliable adaptation to game circumstances. We named the approach 'case-based adaptive game AI'. In the approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource-intensive learning) to evoke effective behaviour in a controlled manner in online play. In the research discussed in this article we investigate to what extent incorporating opponent modelling enhances the performance of case-based adaptive game AI. In our approach, models of the opponent players are generated automatically, on the basis of observations drawn from a multitude of games. We performed experiments that test the enhanced approach in an actual, complex RTS game, and observed that the effectiveness of case-based adaptive game AI increases significantly when opponent modelling is incorporated. From these results we may conclude that opponent modelling further improves the basis for implementation of case-based adaptive game AI in commercially available video games.