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This self-contained introductory text on the behavior of learning automata focuses on how a sequential decision-maker with a finite number of choices responds in a random environment. Topics include fixed structure automata, variable structure stochastic automata, convergence, 0 and S models, nonstationary environments, interconnected automata and games, and applications of learning automata. A must for all students of stochastic algorithms, this treatment is the work of two well-known scientists and is suitable for a one-semester graduate course in automata theory and stochastic algorithms. This volume also provides a fine guide for independent study and a reference for students and professionals in operations research, computer science, artificial intelligence, and robotics. The authors have provided a new preface for this edition.
This self-contained introductory text on the behavior of learning automata focuses on how a sequential decision-maker with a finite number of choices responds i
This book collects recent theoretical advances and concrete applications of learning automata (LAs) in various areas of computer science, presenting a broad tre
This self-contained introductorytext on the behavior of learningautomata focuses on howa sequential decision-makerwith a finite number of choiceswould respond i
Networks of Learning Automata: Techniques for Online Stochastic Optimization is a comprehensive account of learning automata models with emphasis on multiautoma
The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independe
