Zhang JunQi, Zhou MengChu. Learning Automata and Their Applications to Intelligent Systems

Wiley-IEEE Press, 2024. — 275 p. — ISBN: 978-1-394-18852-9.Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata. As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application. The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested. A timely text in a rapidly developing field, Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate-level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.Learning Automata.
Fast Learning Automata.
Application-Oriented Learning Automata.
Ordinal Optimization.
Incorporation of Ordinal Optimization into Learning Automata.
Noisy Optimization Applications.
Applications and Future Research Directions of Learning Automata.