Recent Advances in Learning Automata

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.

List of 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.

Summary

Product details

• Authors: Mehdi Esnaashari, Mohammad Reza Meybodi, Alirez Rezvanian, Alireza Rezvanian, Ali Mohamma Saghiri, Ali Mohammad Saghiri, Vahidipou, Seyed Mehdi Vahidipour
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 01.01.2018
• EAN: 9783319724270
• ISBN: 978-3-31-972427-0
• No. of pages: 458
• Dimensions: 163 mm x 241 mm x 33 mm
• Weight: 876 g
• Illustrations: XIX, 458 p. 240 illus., 126 illus. in color.
• Series: Studies in Computational Intelligence; Studies in Computational Intelligence
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; B, Artificial Intelligence, engineering, Computational Intelligence, Cellular Learning Automata, Distributed Learning Automata, Estimator Learning Automata, Interconnected Learning Automata, Games of Learning Automata