Using State Estimation for Dynamic Agent Modelling
Full Text: bard.msc.pdf
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.
Citation
N. Bard. "Using State Estimation for Dynamic Agent Modelling". MSc Thesis, University of Alberta, March 2008.| Keywords: | state estimation, agent modelling, opponent modelling, Bayesian filtering, probabilistic inference |
| Category: | MSc Thesis |
| Related Publication(s): | Particle Filtering for Dynamic Agent Modelling in Simplified Poker |
BibTeX
@mastersthesis{Bard:08,
author = {Nolan Bard},
title = {Using State Estimation for Dynamic Agent Modelling},
School = {University of Alberta},
year = 2008,
}Last Updated: August 21, 2009Submitted by Nolan Bard
