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Computing Optimal Strategies to Commit to in Stochastic Games Joshua Letchford1 and Liam MacDermed2 and Vincent Conitzer1 and Ronald Parr1 and Charles L. Isbell2 1 Duke University, Department of Computer Science, Durham,
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Document Date: 2013-04-16 11:50:53

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File Size: 1,08 MB

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City

Los Angeles / IRIS / Durham / Atlanta / /

Company

3SAT / Intelligent Machines Laboratory / /

Country

United States / /

Currency

Rs / /

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Facility

Duke University / Carnegie Mellon University / Georgia Institute of Technology / Los Angeles International Airport / /

IndustryTerm

similar utilities / game-theoretic solutions / possible continuation utilities / computing / commitment solution / transportation networks / maximal utilities / /

Organization

Duke University / National Science Foundation / Department of Computer Science / Georgia Institute of Technology / Artificial Copyright Intelligence / Carnegie Mellon University / Association for the Advancement / /

Person

Chen / Kalai / Deng / Stackelberg / Murray / Nash / Gordon / /

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Position

state player / wants player / causing player / time player / give player / convincing player / leader / both theoretically and experimentally / signal player / learning player / Coast Guard / row player / player / action player / benefiting player / /

ProvinceOrState

Kansas / North Carolina / Georgia / /

PublishedMedium

Games and Economic Behavior / /

Technology

QPACE algorithm / artificial intelligence / 6.1 Computing Commitment Equilibria The QPACE algorithm / Correlated Equilibria algorithm / /

URL

www.aaai.org / /

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