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Possibilistic Games with Incomplete Information (IJCAI 2019)

Abstract : Bayesian games offer a suitable framework for games where the utility degrees are additive. This approach does nevertheless not apply to ordinal games, where the utility degrees do not capture more than a ranking, nor to situations of a decision under qualitative uncertainty. This paper proposes a representation framework for ordinal games under possibilistic incomplete information and extends the fundamental notion of Nash equilibrium (NE) to this framework. We show that deciding whether a NE exists is a difficult problem (NP-hard) and propose a Mixed Integer Linear Programming encoding. Experiments on variants of the GAMUT problems confirm the feasibility of this approach.
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Submitted on : Thursday, November 28, 2019 - 7:09:15 PM
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Nahla Ben Amor, Hélène Fargier, Régis Sabbadin, Meriem Trabelsi. Possibilistic Games with Incomplete Information (IJCAI 2019). Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Aug 2019, Macao, China. pp.1544-1550, ⟨10.24963/ijcai.2019/214⟩. ⟨hal-02879715v2⟩



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