抄録
Stochastic Learning Automata (LA) are probabilistic finite state machines which have been used to model how biological systems can learn. The structure of such a machine can be fixed, or it can be changing with time A LA can also be implemented by using action probability updating rules which may or may not depend on estimates from the Environment being investigated. During the initial years of research in the field of LA, these updating rules worked with the continuous probability space. In this paper, we will describe how LA can also be designed by discretizing the probability space. The paper1 will describe the design and analysis of both continuous and discretized LA, and will highlight the subtle differences between the corresponding learning machines, their convergence properties, and their learning capabilities.
