Strategic Negotiations for Extensive-form Games

When studying extensive-form games it is commonly assumed that players make their decisions individually. One usually does not allow the possibility for the players to negotiate their respective strategies and formally commit themselves to future moves. As a consequence, many non-zero-sum games have...

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Detalles Bibliográficos
Autor: De Jonge, Dave
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/235092
Acceso en línea:http://hdl.handle.net/10261/235092
Access Level:acceso abierto
Palabra clave:Automated negotiations
Non-zero-sum games
Extensive-form games
General game playing
Monte Carlo tree search
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spelling Strategic Negotiations for Extensive-form GamesDe Jonge, DaveAutomated negotiationsNon-zero-sum gamesExtensive-form gamesGeneral game playingMonte Carlo tree searchWhen studying extensive-form games it is commonly assumed that players make their decisions individually. One usually does not allow the possibility for the players to negotiate their respective strategies and formally commit themselves to future moves. As a consequence, many non-zero-sum games have been shown to have equilibrium outcomes that are suboptimal and arguably counter-intuitive. For this reason we feel there is a need to explore a new line of research in which game-playing agents are allowed to negotiate binding agreements before they make their moves. We analyze what happens under such assumptions and define a new equilibrium solution concept to capture this. We show that this new solution concept indeed yields solutions that are more efficient and, in a sense, closer to what one would expect in the real world. Furthermore, we demonstrate that our ideas are not only theoretical in nature, but can also be implemented on bounded rational agents, with a number of experiments conducted with a new algorithm that combines techniques from Automated Negotiations, (Algorithmic) Game Theory, and General Game Playing. Our algorithm, which we call Monte Carlo Negotiation Search, is an adaptation of Monte Carlo Tree Search that equips the agent with the ability to negotiate. It is completely domain-independent in the sense that it is not tailored to any specific game. It can be applied to any non-zero-sum game, provided that its rules are described in Game Description Language. We show with several experiments that it strongly outperforms non-negotiating players, and that it closely approximates the theoretically optimal outcomes, as defined by our new solution concept.Kluwer Academic PublishersConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2021202120202021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/235092reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1007/s10458-019-09424-ySíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2350922026-05-22T06:33:51Z
dc.title.none.fl_str_mv Strategic Negotiations for Extensive-form Games
title Strategic Negotiations for Extensive-form Games
spellingShingle Strategic Negotiations for Extensive-form Games
De Jonge, Dave
Automated negotiations
Non-zero-sum games
Extensive-form games
General game playing
Monte Carlo tree search
title_short Strategic Negotiations for Extensive-form Games
title_full Strategic Negotiations for Extensive-form Games
title_fullStr Strategic Negotiations for Extensive-form Games
title_full_unstemmed Strategic Negotiations for Extensive-form Games
title_sort Strategic Negotiations for Extensive-form Games
dc.creator.none.fl_str_mv De Jonge, Dave
author De Jonge, Dave
author_facet De Jonge, Dave
author_role author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Automated negotiations
Non-zero-sum games
Extensive-form games
General game playing
Monte Carlo tree search
topic Automated negotiations
Non-zero-sum games
Extensive-form games
General game playing
Monte Carlo tree search
description When studying extensive-form games it is commonly assumed that players make their decisions individually. One usually does not allow the possibility for the players to negotiate their respective strategies and formally commit themselves to future moves. As a consequence, many non-zero-sum games have been shown to have equilibrium outcomes that are suboptimal and arguably counter-intuitive. For this reason we feel there is a need to explore a new line of research in which game-playing agents are allowed to negotiate binding agreements before they make their moves. We analyze what happens under such assumptions and define a new equilibrium solution concept to capture this. We show that this new solution concept indeed yields solutions that are more efficient and, in a sense, closer to what one would expect in the real world. Furthermore, we demonstrate that our ideas are not only theoretical in nature, but can also be implemented on bounded rational agents, with a number of experiments conducted with a new algorithm that combines techniques from Automated Negotiations, (Algorithmic) Game Theory, and General Game Playing. Our algorithm, which we call Monte Carlo Negotiation Search, is an adaptation of Monte Carlo Tree Search that equips the agent with the ability to negotiate. It is completely domain-independent in the sense that it is not tailored to any specific game. It can be applied to any non-zero-sum game, provided that its rules are described in Game Description Language. We show with several experiments that it strongly outperforms non-negotiating players, and that it closely approximates the theoretically optimal outcomes, as defined by our new solution concept.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/235092
url http://hdl.handle.net/10261/235092
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1007/s10458-019-09424-y
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Kluwer Academic Publishers
publisher.none.fl_str_mv Kluwer Academic Publishers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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