Negotiation and cooperation in multi-agent environments
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Online computation and competitive analysis
Online computation and competitive analysis
Algorithmic mechanism design (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Equilibrium analysis of the possibilities of unenforced exchange in multiagent systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
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Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite unclear. Most work in AI adopts classical game-theoretic equilibrium analysis to predict agent behavior in such settings. Needless to say, this approach does not provide us with any guarantee for the agent. In this paper we introduce competitive safety analysis. This approach bridges the gap between the desired normative AI approach, where a strategy should be selected in order to guarantee a desired payoff, and equilibrium analysis. We show that a safety level strategy is able to guarantee the value obtained in a Nash equilibrium, in several classical computer science settings. Then, we discuss the concept of competitive safety strategies, and illustrate its use in a decentralized load balancing setting, typical to network problems. In particular, we show that when we have many agents, it is possible to guarantee an expected payoff which is a factor of 8/9 of the payoff obtained in a Nash equilibrium. Finally, we discuss the extension of the above concepts to Bayesian games, and illustrate their use in a basic auctions setup.