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This paper provides a framework for a class of methods to solve the adaptive load balancing problem in flexible manufacturing systems. The control system is composed of a group of associative learning automata which interact with each other in a game-theoretic sense. Each automaton makes use of a global reinforcement signal for learning the control strategy under different state input. The control actions suggested by the automata interact through a constraint satisfaction network to give a globally legal set of control actions. Using existing techniques in neural network research, we propose one particular method of the class by implementing both the associative reinforcement learning and the constraint satisfaction modules by connectionist networks. Comparisons of this method with other related studies will be discussed. We expect our current simulation work to provide empirical support for future analytical study.