Learning Automata - A Survey

Stochastic automata operating in an unknown random can be considered to show learning behavior. Tsypkin environment have been proposed earlier as models of learning. These [GT1] has recently argued that seemingly diverse problems automata update their action probabilities in accordance with the inputs . . . received from the environment and can improve their own performance inpa t rec i o idenfatio n lering. during operation. In this context they are referred to as learning autocan be treated ii a unified manner as problems in learning mata. A survey of the available results in the area of learning automata using probabilistic iterative methods. has been attempted in this paper. Attention has been focused on the Viewed in a purely mathematical context the goal of a norms of behavior of learning automata, issues in the design of updating learning system is the optimization of a functional not schemes, convergence of the action probabilities, and interaction of . . several automata. Utilization of learning automata in parameter known expicily,asfunctoeamlwith athemati daltexpeta-n optimization and hypothesis testing is discussed, and potential areas o tion of a random functional with a probability distribution application are suggested. function not known in advance. An approach that has been used in the past is to reduce the problem to the determina