Abstract
This paper presents an experimental verification of a novel, fast and arbitrarily accurate solution to the Goore Game (GG). The latter game, introduced in [6], has the fascinating property that it can be resolved in a completely distributed manner with no inter-communication between the players. The game has recently found applications in many domains, including the field of sensor networks and Quality-of-Service (QoS) routing. In actual implementations of the solution, the players are typically replaced by Learning Automata (LA). The problem with the existing reported approaches is that the accuracy of the solution achieved is intricately related to the number of players participating in the game – which, in turn, determines the resolution, implying that arbitrary accuracy can be obtained only if the game has an infinite number of players. In this paper, we experimental demonstrate how we can attain an unbounded accuracy for the GG by utilizing no more than three stochastic learning machines, and by a recursive pruning of the solution space.
This work was partially supported by NSERC, the Natural Sciences and Engineering Research Council of Canada.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Iyer, R., Kleinrock, L.: Qos control for sensor networks. In: IEEE International Conference on Communications, vol. 1, pp. 517–521 (2003)
Narendra, K.S., Thathachar, M.A.L.: Learning Automata. Prentice-Hall, Englewood Cliffs (1989)
Oommen, B.J., Raghunath, G., Kuipers, B.: Parameter learning from stochastic teachers and stochastic compulsive liars. IEEE Transactions on Systems Man and Cybernetics (to appear, 2006)
Thathachar, M.A.L., Arvind, M.T.: Solution of Goore game using models of stochastic learning automata. J. Indian Institute of Science (76), 47–61 (1997)
Thathachar, M.A.L.T., Sastry, P.S.: Networks of Learning Automata: Techniques for Online Stochastic Optimization. Kluwer Academic, Boston (2003)
Tsetlin, M.L.: Automaton Theory and the Modelling of Biological Systems. Academic Press, New York and London (1973)
Tung, B., Kleinrock, L.: Using Finite State Automata to Produce Self-Optimization and Self-Control. IEEE Transactions on Parallel and Distributed Systems 7(4), 47–61 (1996)
Oommen, B.J., Granmo, O.C., Pedersen, A.: Achieving Unbounded Resolution in Finite Player Goore Games using Stochastic Automata, and its Applications. Unabridged version of this paper (submitted for publication, 2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oommen, B.J., Granmo, OC., Pedersen, A. (2006). Empirical Verification of a Strategy for Unbounded Resolution in Finite Player Goore Games. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_156
Download citation
DOI: https://doi.org/10.1007/11941439_156
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
eBook Packages: Computer ScienceComputer Science (R0)