Equilibrium dynamic systems intelligence

Most work in Artificial Intelligence reviews the balance of classic game theory to predict agent behavior in different positions. In this paper we introduce steady competitive analysis. This approach bridges the gap between the standards of desired paths of artificial intelligence, where a strategy must 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 will discuss the concept of competitive strategy and illustrate how it is used in a decentralized load balanced position, typical for network problems. In particular, we will show that when there are many agents, it is possible to guarantee an expected final result, which is a 8/9 factor of the final result obtained in the Nash equilibrium. Finally, we will discuss about extending the above concept in Bayesian game and illustrate its use in a basic structure of an auction.

[1]  Jason M. Kinser,et al.  Image Processing using Pulse-Coupled Neural Networks , 1998, Perspectives in Neural Computing.

[2]  A. Roth.  Values for Games without Sidepayments: Some Difficulties with Current Concepts , 1980 .

[3]  A. Mas-Colell,et al.  Microeconomic Theory , 1995 .

[4]  Tim Roughgarden,et al.  How bad is selfish routing? , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[5]  Christos H. Papadimitriou,et al.  Worst-case Equilibria , 1999, STACS.

[6]  Victor R. Lesser,et al.  Equilibrium Analysis of the Possibilities of Unenforced Exchange in Multiagent Systems , 1995, IJCAI.

[7]  Bruce Powel Douglass.  Real Time UML: Advances in the UML for Real-Time Systems (3rd Edition) , 2004 .

[8]  Tim Roughgarden,et al.  How bad is selfish routing? , 2002, JACM.

[9]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[10]  Moshe Tennenholtz,et al.  Rational Competitive Analysis , 2001, IJCAI.

[11]  Noam Nisan,et al.  Algorithmic mechanism design (extended abstract) , 1999, STOC '99.

[12]  K. R. Rao,et al.  Techniques and Standards for Image, Video, and Audio Coding , 1996 .

[13]  Bruce Powel Douglass.  Real-Time UML , 2002, FTRTFT.

[14]  Ron Lavi,et al.  Algorithmic Mechanism Design , 2008, Encyclopedia of Algorithms.

[15]  M. Dufwenberg.  Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[16]  Lawrence Harte,et al.  GPS Quick Course 2nd Edition, Systems, Technology and Operation , 2009 .

[17]  Tim Roughgarden,et al.  The price of anarchy is independent of the network topology , 2002, STOC '02.

[18]  Allan Borodin,et al.  Online computation and competitive analysis , 1998 .

[19]  Marius-Constantin Popescu,et al.  Fuzzy control algorithm implementation using LabWindows , 2009 .

[20]  Luminita Popescu,et al.  The simulation hybrid fuzzy control of SCARA robot , 2008 .

[21]  Ilya Segal,et al.  Solutions manual for Microeconomic theory : Mas-Colell, Whinston and Green , 1997 .

[22]  R. Rosenthal.  A class of games possessing pure-strategy Nash equilibria , 1973 .

[23]  Sarit Kraus,et al.  Negotiation and Cooperation in Multi-Agent Environments , 1997, Artif. Intell..

[24]  R. Aumann.  On the Non-transferable Utility Value: A Comment on the Roth-Shafer Examples [Values for Games without Side Payments: Some Difficulties with Current Concepts] [On the Existence and Interpretation of Value Allocations] , 1985 .

[25]  Jeffrey S. Rosenschein and Gilad Zlotkin.  Rules of Encounter , 1994 .

[26]  L. Shapley,et al.  Potential Games , 1994 .

[27]  Pradeep Tapadiya.  .NET Programming: A Practical Guide Using C# , 2002 .