Using Bayesian networks to model the belief in the opponent in static game with incomplete information

Noncooperative game theory provides a normative framework for analyzing strategic interactions of agents. In some noncooperative games agent may be lack of information about its opponents. So it must make decisions on uncertain opponents. In this paper, Bayesian network is used to model the agent uncertainty of its opponents. The uncertainty can be updated when some events happen through Bayesian network.