Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
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
Journal of the ACM (JACM)
The price of anarchy is independent of the network topology
STOC '02 Proceedings of the thiry-fourth 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
How much can taxes help selfish routing?
Proceedings of the 4th ACM conference on Electronic commerce
On cheating in sealed-bid auctions
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Game-theoretic recommendations: some progress in an uphill battle
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Artificial Intelligence
Journal of Artificial Intelligence Research
Efficiently exploiting symmetries in real time dynamic programming
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On cheating in sealed-bid auctions
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Competitive safety strategies in position auctions
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Journal of Artificial Intelligence Research
OPODIS'11 Proceedings of the 15th international conference on Principles of Distributed Systems
Hi-index | 0.00 |
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. This approach however 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. Our discussion of competitive safety analysis for decentralized load balancing is further developed to deal with many communication links and arbitrary speeds. Finally, we discuss the extension of the above concepts to Bayesian games, and illustrate their use in a basic auctions setup.