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
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
Multilayer perceptron and neural networks
WSEAS Transactions on Circuits and Systems
Applications of genetic algorithms
WSEAS Transactions on Information Science and Applications
The use of MIMO technologies in wireless communication networks
CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
Solving applications by use of genetic algorithms
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
| Hi-index | 0.00 |
Most work in Artificial Intelligence shall review the balance of classic game theory to predict agent behavior in such positions. In this paper introduce sure competitive analysis. This approach by bridging the gap between the norms of the desired path of artificial intelligence, where a strategy should be selected in order to ensure an end result and a balanced analysis. We show that a strategy without risk level is able to guarantee the value obtained in the Nash equilibrium, by more scientific methods of classical computers. Then we discuss the concept of competitive strategy and secure we illustrate how it is used in a decentralized load balanced position, typical network problems. In particular, show that when we have many agents, it is possible to guarantee a final result expected, which is a factor of 8/9 of the final result obtained in the Nash equilibrium. Finally we discuss extending the above concept in Bayesian game and illustrated their use in a basic structure of the auction.