Information Set Generation in Partially Observable Games
DOI:
https://doi.org/10.1609/aaai.v26i1.8146Keywords:
game tree search, game theory, information sets, probabilistic reasoning, constraint satisfaction, searchAbstract
We address the problem of making single-point decisions in large partially observable games, where players interleave observation, deliberation, and action. We present information set generation as a key operation needed to reason about games in this way. We show how this operation can be used to implement an existing decision-making algorithm. We develop a constraint satisfaction algorithm for performing information set generation and show that it scales better than the existing depth-first search approach on multiple non-trivial games.
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Published
2021-09-20
How to Cite
Richards, M., & Amir, E. (2021). Information Set Generation in Partially Observable Games. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 549-555. https://doi.org/10.1609/aaai.v26i1.8146
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Section
Constraints, Satisfiability, and Search