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

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP202%2F12%2F2054" target="_blank" >GAP202/12/2054: Bezpečnostní hry v extenzivní formě</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Autonomous Agents and Multiagent Systems

ISBN

978-1-4503-3771-7

ISSN

1548-8403

e-ISSN

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Počet stran výsledku

10

Strana od-do

27-36

Název nakladatele

IFAAMAS

Místo vydání

County of Richland

Místo konání akce

Istanbul

Datum konání akce

4. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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