A formal theory of plan recognition and its implementation
Reasoning about plans
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Effective short-term opponent exploitation in simplified poker
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Particle filters for state-space models with the presence ofunknown static parameters
IEEE Transactions on Signal Processing
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
Hi-index | 0.01 |
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 paper looks at the use of particle filtering for modelling a dynamic opponent 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 as well as 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.