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arxiv: Computer Science::Computer Science and Game Theory
Distributed optimisation techniques have gained increasing attention due to fast development of autonomous robots. Many algorithms have been proposed to make optimisation more efficient. In this paper we propose a framework, which is based on probabilistic verification techniques, in order to compare the performance of various game-theoretic algorithms, in particular, fictitious play and its variants, after a finite number of iterations. To demonstrate the effectiveness of the framework, we apply the framework to a game which is inspired by wireless communication network problems, on five variations of fictitious play algorithms.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Brown G. W., Iterative Solutions of Games by Fictitious Play, 1951; In Activity Analysis of Production and Allocation, p. 374- 376, Wiley.
    • 2. Fudenberg D., Levine D. S., The Theory of Learning in Games, 1998, The MIT Press.
    • 3. Smyrnakis M., Leslie, D. S., Adaptive Forgetting Factor Fictitious Play.
    • 4. Smyrnakis M., Veres, S., Coordination of Control in Robot Teams Using Game-theoretic Learning, IFAC, 2014.
    • 5. Smyrnakis M., Leslie D. S., Dynamic Opponent Modelling in Fictitious Play, 2010; The Computer Journal 53(9):1344-1359.
    • 6. Courcoubetis C., Yannakakis M., Verifying Temporal Properties of Finite State Probabilistic Programs, 1988; In proceedings of FOCS'88, p. 338-345, IEEE Computer Society Press.
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