Opponent Modelling and Commercial Games

To play a game well a player needs to understand the game. To defeat an opponent, it may be sufficient to understand the opponent’s weak spots and to be able to exploit them. In human practice, both elements (knowing the game and knowing the opponent) play an important role. This article focuses on opponent modelling independent of any game. So, the domain of interest is a collection of two-person games, multiperson games, and commercial games. The emphasis is on types and roles of opponent models, such as speculation, tutoring, training, and mimicking characters. Various implementations are given. Suggestions for learning the opponent models are described and their realization is illustrated by opponent models in game-tree search. We then transfer these techniques to commercial games. Here it is crucial for a successful opponent model that the changes of the opponent’s reactions over time are adequately dealt with. This is done by dynamic scripting, an improvised online learning technique for games. Our conclusions are (1) that opponent modelling has a wealth of techniques that are waiting for implementation in actual commercial games, but (2) that the games’ publishers are reluctant to incorporate these techniques since they have no definitive opinion on the successes of a program that is outclassing human beings in strength and creativity, and (3) that game AI has an entertainment factor that is too multifaceted to grasp in reasonable time.