@article{Hawkin_Holte_Szafron_2011, title={Automated Action Abstraction of Imperfect Information Extensive-Form Games}, volume={25}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7880}, DOI={10.1609/aaai.v25i1.7880}, abstractNote={ <p> Multi-agent decision problems can often be formulated as extensive-form games. We focus on imperfect information extensive-form games in which one or more actions at many decision points have an associated continuous or many-valued parameter. A stock trading agent, in addition to deciding whether to buy or not, must decide how much to buy. In no-limit poker, in addition to selecting a probability for each action, the agent must decide how much to bet for each betting action. Selecting values for these parameters makes these games extremely large. Two-player no-limit Texas Hold’em poker with stacks of 500 big blinds has approximately 10<sup>71</sup> states, which is more than 10<sup>50</sup> times more states than two-player limit Texas Hold’em. The main contribution of this paper is a technique that abstracts a game’s action space by selecting one, or a small number, of the many values for each parameter. We show that strategies computed using this new algorithm for no-limit Leduc poker exhibit significant utility gains over epsilon-Nash equilibrium strategies computed with standard, hand-crafted parameter value abstractions. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Hawkin, John and Holte, Robert and Szafron, Duane}, year={2011}, month={Aug.}, pages={681-687} }