Rezvanian / Saghiri / Vahidipour Recent Advances in Learning Automata

1. Auflage 2018
ISBN: 978-3-319-72428-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 754, 458 Seiten, eBook

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Rezvanian, Alireza

Saghiri, Ali Mohammad

Vahidipour, Seyed Mehdi

Esnaashari, Mehdi

Meybodi, Mohammad Reza

Inhaltsverzeichnis

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.