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Recent Advances in Learning Automata. Softcover reprint of the original 1st ed. 2018
・ISBN 978-3-319-89182-8 paper EUR 99.99
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| 著者・編者 | Rezvanian, Alireza / Saghiri, Ali Mohammad / Vahidipour, Seyed Mehdi / Esnaashari, Mehdi / Meybodi, Mohammad Reza, |
|---|---|
| シリーズ | (Studies in Computational Intelligence) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2019 |
| ページ数 | 458 pp. |
| 言語 | ENG |
| ニュース番号 | <A04-60705> |
解説
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.