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Recent Advances in Learning Automata [electronic resource] / by Alireza Rezvanian, Ali Mohammad Saghiri, Seyed Mehdi Vahidipour, Mehdi Esnaashari, Mohammad Reza Meybodi.

By: Rezvanian, Alireza [author.].
Contributor(s): Saghiri, Ali Mohammad [author.] | Vahidipour, Seyed Mehdi [author.] | Esnaashari, Mehdi [author.] | Meybodi, Mohammad Reza [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 754Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XIX, 458 p. 240 illus., 126 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319724287.Subject(s): Computational intelligence | Artificial intelligence | Computational Intelligence | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Learning automata theory -- Cellular learning automata -- Learning automata for wireless sensor networks -- Learning automata for cognitive Peer-to-peer networks -- Learning automata for Complex Social Networks -- Adaptive petri net based on learning automata -- Summary and future directions.
In: Springer Nature eBookSummary: 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.
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Learning automata theory -- Cellular learning automata -- Learning automata for wireless sensor networks -- Learning automata for cognitive Peer-to-peer networks -- Learning automata for Complex Social Networks -- Adaptive petri net based on learning automata -- Summary and future directions.

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.

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