Possibilistic Games with Incomplete Information
Possibilistic Games with Incomplete Information
Nahla Ben Amor, Helene Fargier, Régis Sabbadin, Meriem Trabelsi
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1544-1550.
https://doi.org/10.24963/ijcai.2019/214
Bayesian games offer a suitable framework for
games where the utility degrees are additive in
essence. This approach does nevertheless not apply to ordinal games, where the utility degrees do
not capture more than a ranking, nor to situations
of decision under qualitative uncertainty. This paper proposes a representation framework for ordinal games under possibilistic incomplete information (π-games) 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 (MILP) encoding.
Experiments on variants of the GAMUT problems
confirm the feasibility of this approach.
Keywords:
Knowledge Representation and Reasoning: Qualitative Reasoning
Agent-based and Multi-agent Systems: Noncooperative Games
Uncertainty in AI: Uncertainty in AI
Knowledge Representation and Reasoning: Knowledge Representation and Game Theory ; Social Choice
Agent-based and Multi-agent Systems: Algorithmic Game Theory
Uncertainty in AI: Nonprobabilistic Models