Recent Advances in Learning Automata (Hardcover, 1st ed. 2018)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 754
Release date:	2018
First published:	2018
Authors:	Alireza Rezvanian • Ali Mohammad Saghiri • Seyed Mehdi Vahidipour • Mehdi Esnaashari • Mohammad Reza Meybodi
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	458
Edition:	1st ed. 2018
ISBN-13:	978-3-319-72427-0
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-319-72427-4
Barcode:	9783319724270

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