A CBR-Inspired Approach to Rapid and Reliable Adaption of Video Game AI

Current approaches to adaptive game AI typically require numerous trials to learn effective behaviour (i.e., game adaptation is not rapid). In addition, game developers are concerned that applying adaptive game AI may result in uncontrollable and unpredictable behaviour (i.e., game adaptation is not reliable). These characteristics hamper the incorporation of adaptive game AI in commercially available video games. In this article, we discuss an alternative to these approaches. In the case-based inspired 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. We performed experiments that test case-based adaptive game AI on three different maps in a commercial RTS game. From our results we may conclude that case-based adaptive game AI provides a strong basis for effectively adapting game AI in video games.