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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 28 / No. 1: Twenty-Eighth AAAI Conference On Artificial Intelligence

Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games

March 8, 2023

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Authors

Sam Ganzfried

Carnegie Mellon University


Tuomas Sandholm

Carnegie Mellon University


DOI:

10.1609/aaai.v28i1.8816


Abstract:

There is often a large disparity between the size of a game we wish to solve and the size of the largest instances solvable by the best algorithms; for example, a popular variant of poker has about $10^{165}$ nodes in its game tree, while the currently best approximate equilibrium-finding algorithms scale to games with around $10^{12}$ nodes. In order to approximate equilibrium strategies in these games, the leading approach is to create a sufficiently small strategic approximation of the full game, called an abstraction, and to solve that smaller game instead. The leading abstraction algorithm for imperfect-information games generates abstractions that have imperfect recall and are distribution aware, using $k$-means with the earth mover's distance metric to cluster similar states together. A distribution-aware abstraction groups states together at a given round if their full distributions over future strength are similar (as opposed to, for example, just the expectation of their strength). The leading algorithm considers distributions over future strength at the final round of the game. However, one might benefit by considering the trajectory of distributions over strength in all future rounds, not just the final round. An abstraction algorithm that takes all future rounds into account is called potential aware. We present the first algorithm for computing potential-aware imperfect-recall abstractions using earth mover's distance. Experiments on no-limit Texas Hold'em show that our algorithm improves performance over the previously best approach.

Topics: AAAI

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HOW TO CITE:

Sam Ganzfried|| Tuomas Sandholm Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games Proceedings of the AAAI Conference on Artificial Intelligence, 28 (2014) .

Sam Ganzfried|| Tuomas Sandholm Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games AAAI 2014, .

Sam Ganzfried|| Tuomas Sandholm (2014). Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games. Proceedings of the AAAI Conference on Artificial Intelligence, 28, .

Sam Ganzfried|| Tuomas Sandholm. Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games. Proceedings of the AAAI Conference on Artificial Intelligence, 28 2014 p..

Sam Ganzfried|| Tuomas Sandholm. 2014. Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games. "Proceedings of the AAAI Conference on Artificial Intelligence, 28". .

Sam Ganzfried|| Tuomas Sandholm. (2014) "Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games", Proceedings of the AAAI Conference on Artificial Intelligence, 28, p.

Sam Ganzfried|| Tuomas Sandholm, "Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games", AAAI, p., 2014.

Sam Ganzfried|| Tuomas Sandholm. "Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games". Proceedings of the AAAI Conference on Artificial Intelligence, 28, 2014, p..

Sam Ganzfried|| Tuomas Sandholm. "Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games". Proceedings of the AAAI Conference on Artificial Intelligence, 28, (2014): .

Sam Ganzfried|| Tuomas Sandholm. Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games. AAAI[Internet]. 2014[cited 2023]; .


ISSN: 2374-3468


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