Recent Advances in Learning Automata

ISBN-13: 9783319724270 / Angielski / Twarda / 2018 / 458 str.

Alireza Rezvanian; Ali Mohammad Saghiri; Seyed Mehdi Vahidipour

Opis

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.

Spis treści

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.

Blurb

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.