Solution of Goore game using modules of stochastic learning automata

Abstract

The Goore game among learning automata serves as a model ror collective decision making under uncertainly can also be used as a tool for stochastic optimization of a function of a discrete variable. This paper presents the analysis of the Goore game where each player uses the Lk-1 algorithm. A one-to-one correspondence is established between Mable stationary points of the algorithm and the Nash equilibria of the game. A parallel algorithm involving a module of learning automata for each player is then presented with the objective of improving the speed performance. A brief analysis of the algorithm is followed by simulation studies that demonstrate the efficieny of the parallel approach.