This source, "Learning Automata and Their Applications to Intelligent Systems," details the theory and practical uses of learning automata (LAs), which are adaptive algorithms designed to make decisions in uncertain environments. The text explores various types of LAs, including fixed and variable structure automata, and those with deterministic or stochastic estimators. A significant portion of the work is dedicated to optimizing computational budget allocation (OCBA) within these systems, especially for scenarios like selecting the best and worst designs or ranking subsets with high efficiency. Furthermore, the source highlights diverse applications of LAs in areas such as noisy optimization problems, particle swarm optimization, knapsack problems, and decision-making in networks, demonstrating their utility in real-world intelligent systems.
