Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00235272" target="_blank" >RIV/68407700:21230/15:00235272 - isvavai.cz</a>
Result on the web
<a href="http://dl.acm.org/citation.cfm?id=2772887" target="_blank" >http://dl.acm.org/citation.cfm?id=2772887</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games
Original language description
Online search in games has been a core interest of artificial intelligence. Search in imperfect information games (e.g., Poker, Bridge, Skat) is particularly challenging due to the complexities introduced by hidden information. In this paper, we presentOnline Outcome Sampling, an online search variant of Monte Carlo Counterfactual Regret Minimization, which preserves its convergence to Nash equilibrium. We show that OOS can overcome the problem of non-locality encountered by previous search algorithmsand perform well against its worst-case opponents. We show that exploitability of the strategies played by OOS decreases as the amount of search time increases, and that preexisting Information Set Monte Carlo tree search (ISMCTS) can get more exploitable over time. In head-to-head play, OOS outperforms ISMCTS in games where non-locality plays a significant role, given a sufficient computation time per move.
Czech name
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Czech description
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Project
<a href="/en/project/GAP202%2F12%2F2054" target="_blank" >GAP202/12/2054: Security Games in Extensive Form</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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