Combining Incremental Strategy Generation and Branch and Bound Search for Computing Maxmin Strategies in Imperfect Recall Games

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00311823" target="_blank" >RIV/68407700:21230/17:00311823 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/citation.cfm?id=3091253" target="_blank" >https://dl.acm.org/citation.cfm?id=3091253</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Combining Incremental Strategy Generation and Branch and Bound Search for Computing Maxmin Strategies in Imperfect Recall Games

Popis výsledku v původním jazyce

Extensive-form games with imperfect recall are an important model of dynamic games where the players are allowed to forget previously known information. Often, imperfect recall games are the result of an abstraction algorithm that simplifies a large game with perfect recall. Unfortunately, solving an imperfect recall game has fundamental problems since a Nash equilibrium does not have to exist. Alternatively, we can seek maxmin strategies that guarantee an expected outcome. The only existing algorithm computing maxmin strategies in two-player imperfect recall games without absentmindedness, however, requires approximating a bilinear mathematical program that is proportional to the size of the whole game and thus has a limited scalability. We propose a novel algorithm for computing maxmin strategies in this class of games that combines this approximate algorithm with an incremental strategy-generation technique designed previously for extensive-form games with perfect recall. Experimental evaluation shows that the novel algorithm builds only a fraction of the game tree and improves the scalability by several orders of magnitude. Finally, we demonstrate that our algorithm can solve an abstracted variant of a large game faster compared to the algorithms operating on the unabstracted perfect-recall variant.

Název v anglickém jazyce

Combining Incremental Strategy Generation and Branch and Bound Search for Computing Maxmin Strategies in Imperfect Recall Games

Popis výsledku anglicky

Extensive-form games with imperfect recall are an important model of dynamic games where the players are allowed to forget previously known information. Often, imperfect recall games are the result of an abstraction algorithm that simplifies a large game with perfect recall. Unfortunately, solving an imperfect recall game has fundamental problems since a Nash equilibrium does not have to exist. Alternatively, we can seek maxmin strategies that guarantee an expected outcome. The only existing algorithm computing maxmin strategies in two-player imperfect recall games without absentmindedness, however, requires approximating a bilinear mathematical program that is proportional to the size of the whole game and thus has a limited scalability. We propose a novel algorithm for computing maxmin strategies in this class of games that combines this approximate algorithm with an incremental strategy-generation technique designed previously for extensive-form games with perfect recall. Experimental evaluation shows that the novel algorithm builds only a fraction of the game tree and improves the scalability by several orders of magnitude. Finally, we demonstrate that our algorithm can solve an abstracted variant of a large game faster compared to the algorithms operating on the unabstracted perfect-recall variant.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA15-23235S" target="_blank" >GA15-23235S: Abstrakce a extenzivní hry s nedokonalou pamětí</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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