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Learning Automata And Stochastic Optimization
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Book Synopsis Learning Automata and Stochastic Optimization by : A.S. Poznyak
Download or read book Learning Automata and Stochastic Optimization written by A.S. Poznyak and published by Springer. This book was released on 1997-03-12 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade there has been a steadily growing need for and interest in computational methods for solving stochastic optimization problems with or wihout constraints. Optimization techniques have been gaining greater acceptance in many industrial applications, and learning systems have made a significant impact on engineering problems in many areas, including modelling, control, optimization, pattern recognition, signal processing and diagnosis. Learning automata have an advantage over other methods in being applicable across a wide range of functions. Featuring new and efficient learning techniques for stochastic optimization, and with examples illustrating the practical application of these techniques, this volume will be of benefit to practicing control engineers and to graduate students taking courses in optimization, control theory or statistics.
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 275 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 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 Reinforcement Learning and Stochastic Optimization by : Warren B. Powell
Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
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.
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 238 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 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 First-order and Stochastic Optimization Methods for Machine Learning by : Guanghui Lan
Download or read book First-order and Stochastic Optimization Methods for Machine Learning written by Guanghui Lan and published by Springer Nature. This book was released on 2020-05-15 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
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 Stochastic Optimization for Large-scale Machine Learning by : Vinod Kumar Chauhan
Download or read book Stochastic Optimization for Large-scale Machine Learning written by Vinod Kumar Chauhan and published by CRC Press. This book was released on 2021-11-18 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Book Synopsis Algorithms, Languages, Automata, and Compilers: A Practical Approach by : Maxim Mozgovoy
Download or read book Algorithms, Languages, Automata, and Compilers: A Practical Approach written by Maxim Mozgovoy and published by Jones & Bartlett Learning. This book was released on 2009-08-19 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms, Languages, Automata, & Compilers A Practical Approach is designed to cover the standard “theory of computing” topics through a strong emphasis on practical applications rather than theorems and proofs. Finite automata, Turing machines, models of computation, complexity, solvability, and other topics that form a foundation of modern programming are discussed -first with a gentle theoretical orientation, and then applied through programming code and practical examples. JFLAP projects and applications are integrated throughout the book, and C# is used for all code.
Book Synopsis Simulation-Based Optimization by : Abhijit Gosavi
Download or read book Simulation-Based Optimization written by Abhijit Gosavi and published by Springer. This book was released on 2014-10-30 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
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 Stochastic Optimization by : Kurt Marti
Download or read book Stochastic Optimization written by Kurt Marti and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume includes a selection of refereed papers presented at the GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications", held at the Federal Armed Forces University Munich, May 29 - 31, 1990. The objective of this meeting was to bring together scientists from Stochastic Programming and from those Engineering areas, where Mathematical Programming models are common tools, as e. g. Optimal Structural Design, Power Dispatch, Acid Rain Management etc. The first, theoretical part includes the papers by S. D. Flam. H. Niederreiter, E. Poechinger and R. Schultz. The second part on methods and applications contains the articles by N. Baba, N. Grwe and W. Roemisch, J. Mayer, E. A. Mc Bean and A. Vasarhelyi.
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 Nature. This book was released on 2023-06-16 with total page 1533 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 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 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.
