Title of Thesis: Using State Estimation for Dynamic Agent Modelling Using State Estimation for Dynamic Agent Modelling

Agent modelling is a challenging problem in many modern artificial intelligence applications. The agent modelling task is especially difficult when handling stochastic choices, deliberately hidden information, dynamic agents, and the need for fast learning. State estimation techniques, such as Kalman filtering and particle filtering, have addressed many of these challenges, but have received little attention in the agent modelling literature. This thesis explores the use of particle filtering for modelling dynamic opponents in Kuhn poker, a simplified version of Texas Hold’em poker. We demonstrate effective modelling both against static opponents as well as dynamic opponents, when the dynamics are known. We then examine an application of Rao-Blackwellized particle filtering for doing dual estimation, inferring both the opponent’s state and a model of its dynamics. Finally, we examine the robustness of the approach to incorrect beliefs about the opponent and compare it to previous work on opponent modelling in Kuhn poker.