Overview
- Addresses key issues and topics related to learning automata theories, architectures, models, algorithms, and their applications
- Presents a broad treatment of the computer science field in a survey style
- Highlights recent research advances
Part of the book series: Studies in Computational Intelligence (SCI, volume 754)
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About this book
This book collects recent theoretical advances and concrete applications of learning automata (LAs) in various areas of computer science, presenting a broad treatment of the computer science field in a survey style. Learning automata (LAs) have proven to be effective decision-making agents, especially within unknown stochastic environments. The book starts with a brief explanation of LAs and their baseline variations. It subsequently introduces readers to a number of recently developed, complex structures used to supplement LAs, and describes their steady-state behaviors. These complex structures have been developed because, by design, LAs are simple units used to perform simple tasks; their full potential can only be tapped when several interconnected LAs cooperate to produce a group synergy.
In turn, the next part of the book highlights a range of LA-based applications in diverse computer science domains, from wireless sensor networks, to peer-to-peer networks, to complex social networks, and finally to Petri nets. The book accompanies the reader on a comprehensive journey, starting from basic concepts, continuing to recent theoretical findings, and ending in the applications of LAs in problems from numerous research domains. As such, the book offers a valuable resource for all computer engineers, scientists, and students, especially those whose work involves the reinforcement learning and artificial intelligence domains.
In turn, the next part of the book highlights a range of LA-based applications in diverse computer science domains, from wireless sensor networks, to peer-to-peer networks, to complex social networks, and finally to Petri nets. The book accompanies the reader on a comprehensive journey, starting from basic concepts, continuing to recent theoretical findings, and ending in the applications of LAs in problems from numerous research domains. As such, the book offers a valuable resource for all computer engineers, scientists, and students, especially those whose work involves the reinforcement learning and artificial intelligence domains.
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Table of contents (7 chapters)
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Front Matter
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Models
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Recent Advances in Learning Automata
Authors: Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-72428-7
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-72427-0Published: 26 January 2018
Softcover ISBN: 978-3-319-89182-8Published: 06 June 2019
eBook ISBN: 978-3-319-72428-7Published: 17 January 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIX, 458
Number of Illustrations: 114 b/w illustrations, 126 illustrations in colour