Gathering and utilising domain knowledge in commercial computer games

In practice, adaptive game AI in commercial computer games is seldom implemented, be-cause machine learning techniques require numerous trials to learn effective behaviour. To allow fast adaptation in games, in this paper we describe a means of learning that is inspired by the human capability to solve problems by generalising over limited experiences with the problem domain. We compare three approaches, namely straightforward case-based reasoning, situated case-based reasoning, and a k-nearest neighbour classifier. From the experimental results we conclude that the situated approach performs best, both in representing knowledge and generalising to similar situations.