Possibilistic Games with Incomplete Information
Abstract
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.
Cite
Text
Amor et al. "Possibilistic Games with Incomplete Information." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/214Markdown
[Amor et al. "Possibilistic Games with Incomplete Information." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/amor2019ijcai-possibilistic/) doi:10.24963/IJCAI.2019/214BibTeX
@inproceedings{amor2019ijcai-possibilistic,
title = {{Possibilistic Games with Incomplete Information}},
author = {Amor, Nahla Ben and Fargier, Hélène and Sabbadin, Régis and Trabelsi, Meriem},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {1544-1550},
doi = {10.24963/IJCAI.2019/214},
url = {https://mlanthology.org/ijcai/2019/amor2019ijcai-possibilistic/}
}