Uncertainty in Artificial Intelligence
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Regret Minimizing Equilibria and Mechanisms for Games with Strict Type Uncertainty
Nathanael Hyafil, Craig Boutilier
Abstract:
Mechanism design has found considerable application to the construction of agent-interaction protocols. In the standard setting, the type (e.g., utility function) of an agent is not known by other agents, nor is it known by the mechanism designer. When this uncertainty is quantified probabilistically, a mechanism induces a game of incomplete information among the agents. However, in many settings, uncertainty over utility functions cannot easily be quantified. We consider the problem of incomplete information games in which type uncertainty is strict or unquantified. We propose the use of minimax regret as a decision criterion in such games, a robust approach for dealing with type uncertainty. We define minimax-regret equilibria and prove that these exist in mixed strategies for finite games. We also consider the problem of mechanism design in this framework by adopting minimax regret as an optimization criterion for the designer itself, and study automated optimization of such mechanisms.
Keywords: null
Pages: 268-277
PS Link:
PDF Link: /papers/04/p268-hyafil.pdf
BibTex:
@INPROCEEDINGS{Hyafil04,
AUTHOR = "Nathanael Hyafil and Craig Boutilier",
TITLE = "Regret Minimizing Equilibria and Mechanisms for Games with Strict Type Uncertainty",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "268--277"
}


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