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Networks Of Learning Automata
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Book Synopsis Networks of Learning Automata by : M.A.L. Thathachar
Download or read book Networks of Learning Automata written by M.A.L. Thathachar and published by Springer Science & Business Media. This book was released on 2011-06-27 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks of Learning Automata: Techniques for Online Stochastic Optimization is a comprehensive account of learning automata models with emphasis on multiautomata systems. It considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining probabilities of selecting actions. Mathematical analysis of the behavior of games and feedforward networks is provided. Algorithms considered here can be used for online optimization of systems based on noisy measurements of performance index. Also, algorithms that assure convergence to the global optimum are presented. Parallel operation of automata systems for improving speed of convergence is described. The authors also include extensive discussion of how learning automata solutions can be constructed in a variety of applications.
Book Synopsis Recent Advances in Learning Automata by : Alireza Rezvanian
Download or read book Recent Advances in Learning Automata written by Alireza Rezvanian and published by Springer. This book was released on 2018-01-17 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning Automata Approach for Social Networks by : Alireza Rezvanian
Download or read book Learning Automata Approach for Social Networks written by Alireza Rezvanian and published by Springer. This book was released on 2019-01-22 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Cellular Learning Automata: Theory and Applications by : Reza Vafashoar
Download or read book Cellular Learning Automata: Theory and Applications written by Reza Vafashoar and published by Springer Nature. This book was released on 2020-07-24 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning Automata by : Kumpati S. Narendra
Download or read book Learning Automata written by Kumpati S. Narendra and published by Courier Corporation. This book was released on 2013-05-27 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This self-contained introductory text on the behavior of learning automata focuses on how a sequential decision-maker with a finite number of choices responds in a random environment. Topics include fixed structure automata, variable structure stochastic automata, convergence, 0 and S models, nonstationary environments, interconnected automata and games, and applications of learning automata. A must for all students of stochastic algorithms, this treatment is the work of two well-known scientists and is suitable for a one-semester graduate course in automata theory and stochastic algorithms. This volume also provides a fine guide for independent study and a reference for students and professionals in operations research, computer science, artificial intelligence, and robotics. The authors have provided a new preface for this edition.
Book Synopsis Learning Automata and Their Applications to Intelligent Systems by : JunQi Zhang
Download or read book Learning Automata and Their Applications to Intelligent Systems written by JunQi Zhang and published by John Wiley & Sons. This book was released on 2023-12-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata. As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application. The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested. Written by two highly qualified academics with significant experience in the field, Learning Automata and Their Applications to Intelligent Systems covers such topics as: Mathematical analysis of the behavior of learning automata, along with suitable learning algorithms Two application-oriented learning automata: one to discover and track spatiotemporal event patterns, and the other to solve stochastic searching on a line Demonstrations of two pioneering variants of Optimal Computing Budge Allocation (OCBA) methods and how to combine learning automata with ordinal optimization How to achieve significantly faster convergence and higher accuracy than classical pursuit schemes via lower computational complexity of updating the state probability A timely text in a rapidly developing field, Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.
Book Synopsis Intelligent Random Walk: An Approach Based on Learning Automata by : Ali Mohammad Saghiri
Download or read book Intelligent Random Walk: An Approach Based on Learning Automata written by Ali Mohammad Saghiri and published by Springer. This book was released on 2019-01-02 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the intelligent random walk algorithms based on learning automata: these versions of random walk algorithms gradually obtain required information from the nature of the application to improve their efficiency. The book also describes the corresponding applications of this type of random walk algorithm, particularly as an efficient prediction model for large-scale networks such as peer-to-peer and social networks. The book opens new horizons for designing prediction models and problem-solving methods based on intelligent random walk algorithms, which are used for modeling and simulation in various types of networks, including computer, social and biological networks, and which may be employed a wide range of real-world applications.
Book Synopsis Advances in Learning Automata and Intelligent Optimization by : Javidan Kazemi Kordestani
Download or read book Advances in Learning Automata and Intelligent Optimization written by Javidan Kazemi Kordestani and published by Springer Nature. This book was released on 2021-06-23 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs are discussed. Highlighted benefits • Presents the latest advances in learning automata-based optimization approaches. • Addresses the memetic models of learning automata for solving NP-hard problems. • Discusses the application of learning automata for behavior control in evolutionary computation in detail. • Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems.
Book Synopsis Neural and Automata Networks by : E. Goles
Download or read book Neural and Automata Networks written by E. Goles and published by Springer Science & Business Media. This book was released on 2013-03-07 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Et moi ..., si j'avait Sll comment en revenir. One sennce mathematics has rendered the human race. It has put common sense back je n'y serais point alle.' Jules Verne whe", it belongs, on the topmost shelf next to the dusty canister labelled 'discarded non- The series is divergent; therefore we may be smse'. able to do something with it. Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'!ltre of this series
Download or read book Learning Automata written by K. Najim and published by Elsevier. This book was released on 2014-06-28 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning systems have made a significant impact on all areas of engineering problems. They are attractive methods for solving many problems which are too complex, highly non-linear, uncertain, incomplete or non-stationary, and have subtle and interactive exchanges with the environment where they operate. The main aim of the book is to give a systematic treatment of learning automata and to produce a guide to a wide variety of ideas and methods that can be used in learning systems, including enough theoretical material to enable the user of the relevant techniques and concepts to understand why and how they can be used. The book also contains the materials that are necessary for the understanding and development of learning automata for different purposes such as processes identification, optimization and control. Learning Automata: Theory and Applications may be recommended as a reference for courses on learning automata, modelling, control and optimization. The presentation is intended both for graduate students in control theory and statistics and for practising control engineers.
Book Synopsis Study of Neurotron Networks in Learning Automata by : R. E. J. Moddes
Download or read book Study of Neurotron Networks in Learning Automata written by R. E. J. Moddes and published by . This book was released on 1965 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extensive data and mathematical theory are presented on the computer simulation or Probability State Variable (PSV) devices, e.g., the Neurotron, Random State Variable (RSV) Strategy devices, and networks of these elements as a function of the network connectivity, and score and value functions in four problem classes: re-entry trajectory prediction, pattern recognition, inferential measurement techniques, and self-organizing controllers. The simulation models are presented in detail. Hardware implementation is discussed, using statistical source design as a vehicle for insight into cost analysis of the Demonstrator Neurotron and the feasibility of construction of the networks studied. (Author).
Book Synopsis An Introduction to Computational Learning Theory by : Michael J. Kearns
Download or read book An Introduction to Computational Learning Theory written by Michael J. Kearns and published by MIT Press. This book was released on 1994-08-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Book Synopsis Learning Automata by : Kumpati S. Narendra
Download or read book Learning Automata written by Kumpati S. Narendra and published by Courier Corporation. This book was released on 2012-12-19 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This self-contained introductorytext on the behavior of learningautomata focuses on howa sequential decision-makerwith a finite number of choiceswould respond in a random environment. A must for all studentsof stochastic algorithms, this treatment is the workof two well-known scientists, one of whom provides a newIntroduction.Reprint of the Prentice-Hall, Inc, Englewood Cliffs, NewJersey, 1989 edition.
Book Synopsis Grammatical Inference by : Colin de la Higuera
Download or read book Grammatical Inference written by Colin de la Higuera and published by Cambridge University Press. This book was released on 2010-04-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.
Book Synopsis Advances in Neuro-Information Processing by : Mario Köppen
Download or read book Advances in Neuro-Information Processing written by Mario Köppen and published by Springer Science & Business Media. This book was released on 2009-07-10 with total page 1273 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 5506 and LNCS 5507 constitutes the thoroughly refereed post-conference proceedings of the 15th International Conference on Neural Information Processing, ICONIP 2008, held in Auckland, New Zealand, in November 2008. The 260 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. 116 papers are published in the first volume and 112 in the second volume. The contributions deal with topics in the areas of data mining methods for cybersecurity, computational models and their applications to machine learning and pattern recognition, lifelong incremental learning for intelligent systems, application of intelligent methods in ecological informatics, pattern recognition from real-world information by svm and other sophisticated techniques, dynamics of neural networks, recent advances in brain-inspired technologies for robotics, neural information processing in cooperative multi-robot systems.
Book Synopsis Springer Handbook of Automation by : Shimon Y. Nof
Download or read book Springer Handbook of Automation written by Shimon Y. Nof and published by Springer Science & Business Media. This book was released on 2009-07-16 with total page 1841 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook incorporates new developments in automation. It also presents a widespread and well-structured conglomeration of new emerging application areas, such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. The handbook is not only an ideal resource for automation experts but also for people new to this expanding field.
Book Synopsis Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy by : Jose Mira
Download or read book Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy written by Jose Mira and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 5601 and LNCS 5602 constitutes the refereed proceedings of the Third International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2009, held in Santiago de Compostela, Spain, in June 2009. The 108 revised papers presented are thematically divided into two volumes. The first volume includes papers relating the most recent collaborations with Professor Mira and contributions mainly related with theoretical, conceptual and methodological aspects linking AI and knowledge engineering with neurophysiology, clinics and cognition. The second volume contains all the contributions connected with biologically inspired methods and techniques for solving AI and knowledge engineering problems in different application domains.
