Learning fuzzy rules from iterative execution of games
Fuzzy Sets and Systems - Theme: Modeling and learning
| Hi-index | 0.00 |
This paper examines the evolution of unplanned coordination among independent agents in a market selection game, which is a noncooperative repeated game with many agents and several markets. Every agent is supposed to simultaneously choose a single market for maximizing its own payoff obtained by selling its product at the selected market. It is assumed that the market price is determined by the total supply of products. For example, if many agents choose a particular market, the market price at that market is low. The point of the market selection is to choose a market that is not chosen by many other agents. In this paper, game strategies are genetically updated by localized selection and mutation. A new strategy of an agent is probabilistically selected from its neighbors' strategies by the selection operation or randomly updated by the mutation operation. It is shown that the maximization of each agent's payoff leads to the unplanned coordination of the market selection where the undesired concentration of agents is avoided. The unplanned coordination is compared with the planned global coordination obtained by the maximization of the total payoff over all agents