On Markov Games Played by Bayesian and Boundedly-Rational Players

Muthukumaran Chandrasekaran, Yingke Chen, Prashant Doshi

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

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Abstract

We present a new game-theoretic framework in which
Bayesian players with bounded rationality engage in a
Markov game and each has private but incomplete information
regarding other players’ types. Instead of utilizing
Harsanyi’s abstract types and a common prior, we construct
intentional player types whose structure is explicit and induces
a finite-level belief hierarchy. We characterize an equilibrium
in this game and establish the conditions for existence
of the equilibrium. The computation of finding such equilibria
is formalized as a constraint satisfaction problem and its
effectiveness is demonstrated on two cooperative domains.
Original languageEnglish
Title of host publicationProceedings of 31st AAAI Conference on Artificial Intelligence
PublisherAAAI
Number of pages7
Publication statusPublished - 13 Feb 2017
Event31st AAAI Conference on Artificial Intelligence - San Francisco, United States
Duration: 4 Feb 20179 Feb 2017

Publication series

Name Proceedings of the AAAI conference on artificial intelligence
ISSN (Electronic)2374-3468

Conference

Conference31st AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-17
CountryUnited States
CitySan Francisco
Period4/02/179/02/17

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Incomplete information
Constraint satisfaction problem
Bounded rationality
Common priors

Bibliographical note

We present a new game-theoretic framework in which Bayesian players with bounded rationality engage in a Markov game and each has private but incomplete information regarding other players’ types. Instead of utilizing Harsanyi’s abstract types and a common prior, we construct intentional player types whose structure is explicit and induces a finite-level belief hierarchy. We characterize an equilibrium in this game and establish the conditions for existence of the equilibrium. The computation of finding such equilibria is formalized as a constraint satisfaction problem and its effectiveness is demonstrated on two cooperative domains.

Cite this

Chandrasekaran, M., Chen, Y., & Doshi, P. (2017). On Markov Games Played by Bayesian and Boundedly-Rational Players. In Proceedings of 31st AAAI Conference on Artificial Intelligence ( Proceedings of the AAAI conference on artificial intelligence). AAAI.
Chandrasekaran, Muthukumaran ; Chen, Yingke ; Doshi, Prashant. / On Markov Games Played by Bayesian and Boundedly-Rational Players. Proceedings of 31st AAAI Conference on Artificial Intelligence. AAAI, 2017. ( Proceedings of the AAAI conference on artificial intelligence).
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Chandrasekaran, M, Chen, Y & Doshi, P 2017, On Markov Games Played by Bayesian and Boundedly-Rational Players. in Proceedings of 31st AAAI Conference on Artificial Intelligence. Proceedings of the AAAI conference on artificial intelligence, AAAI, 31st AAAI Conference on Artificial Intelligence, San Francisco, United States, 4/02/17.

On Markov Games Played by Bayesian and Boundedly-Rational Players. / Chandrasekaran, Muthukumaran; Chen, Yingke; Doshi, Prashant.

Proceedings of 31st AAAI Conference on Artificial Intelligence. AAAI, 2017. ( Proceedings of the AAAI conference on artificial intelligence).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

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AB - We present a new game-theoretic framework in which Bayesian players with bounded rationality engage in a Markov game and each has private but incomplete information regarding other players’ types. Instead of utilizing Harsanyi’s abstract types and a common prior, we construct intentional player types whose structure is explicit and induces a finite-level belief hierarchy. We characterize an equilibrium in this game and establish the conditions for existence of the equilibrium. The computation of finding such equilibria is formalized as a constraint satisfaction problem and its effectiveness is demonstrated on two cooperative domains.

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Chandrasekaran M, Chen Y, Doshi P. On Markov Games Played by Bayesian and Boundedly-Rational Players. In Proceedings of 31st AAAI Conference on Artificial Intelligence. AAAI. 2017. ( Proceedings of the AAAI conference on artificial intelligence).