Alireza Rezvanian's Books

Learning Automata Approach for Social Networks

By: Alireza Rezvanian,Behnaz Moradabadi,Mina Ghavipour,Mohammad Mehdi Daliri Khomami,Mohammad Reza Meybodi

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Learning Automata Approach for Social Networks

By: Alireza Rezvanian,Behnaz Moradabadi,Mina Ghavipour,Mohammad Mehdi Daliri Khomami,Mohammad Reza Meybodi

This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.

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Page Count

339

Publisher

Springer

Published Date

ISBN 10

3030107671

ISBN 13

9783030107673

Recent Advances in Learning Automata

By: Alireza Rezvanian,Ali Mohammad Saghiri,Seyed Mehdi Vahidipour,Mehdi Esnaashari,Mohammad Reza Meybodi

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Recent Advances in Learning Automata

By: Alireza Rezvanian,Ali Mohammad Saghiri,Seyed Mehdi Vahidipour,Mehdi Esnaashari,Mohammad Reza Meybodi

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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Page Count

471

Publisher

Springer

Published Date

ISBN 10

3319724282

ISBN 13

9783319724287

Cellular Learning Automata: Theory and Applications

By: Reza Vafashoar,Hossein Morshedlou,Alireza Rezvanian,Mohammad Reza Meybodi

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Cellular Learning Automata: Theory and Applications

By: Reza Vafashoar,Hossein Morshedlou,Alireza Rezvanian,Mohammad Reza Meybodi

This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA’s parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments. The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.

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