Paper: Opponent Modeling in Multi-Agent Systems

Opponent Modeling in Multi-Agent Systems

David Carmel and Shaul Markovitch [1996]

Opponent modeling is an interesting way to allow the AI to behave realistically to different strategies. If you can create an accurate model of your opponent, you could be able choose your next actions based on what you predict your opponent will do.

In this paper the authors propose an opponent modeling algorithm based on Finite State Machines that can work in a multi-agent environment.

They explain an algorithm that can create a model of an opponent as a Finite State Machine based on previous actions taken in the game. The model is reinforced as data is being processed. When a counter exapmle is found, the model updates itself to try and match the real model. The full algorithm with proofs and thorough explanations can be found in the original paper.

The authors concluded that their algorithm is only a first step in the area of opponent modeling, but can be of great interest if further research is made.

Complete paper can be found here

David Carmel and Shaul Markovitch. Opponent Modeling in Multi-agent Systems. In Gerhard Weiss and Sandip Sen, editors, Adaption And Learning In Multi-Agent Systems, volume 1042 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1996.