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Author : Enrique Leon Sernik
Publisher :
ISBN 13 :
Total Pages : 378 pages
Book Rating : 4.:/5 (267 download)
Download or read book On the optimal control of partially observed Markov decision models written by Enrique Leon Sernik and published by . This book was released on 1991 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Chong Ho Lee
Publisher :
ISBN 13 :
Total Pages : 154 pages
Book Rating : 4.:/5 (348 download)
Download or read book Optimal Control Limit Policy for a Partially Observable Markov Decision Process Model written by Chong Ho Lee and published by . This book was released on 1994 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Qiying Hu
Publisher : Springer Science & Business Media
ISBN 13 : 0387369511
Total Pages : 305 pages
Book Rating : 4.3/5 (873 download)
Download or read book Markov Decision Processes with Their Applications written by Qiying Hu and published by Springer Science & Business Media. This book was released on 2007-09-14 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Put together by two top researchers in the Far East, this text examines Markov Decision Processes - also called stochastic dynamic programming - and their applications in the optimal control of discrete event systems, optimal replacement, and optimal allocations in sequential online auctions. This dynamic new book offers fresh applications of MDPs in areas such as the control of discrete event systems and the optimal allocations in sequential online auctions.
Author : Marco Wiering
Publisher : Springer Science & Business Media
ISBN 13 : 3642276458
Total Pages : 653 pages
Book Rating : 4.6/5 (422 download)
Download or read book Reinforcement Learning written by Marco Wiering and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.
Author : Vikram Krishnamurthy
Publisher : Cambridge University Press
ISBN 13 : 1107134609
Total Pages : 491 pages
Book Rating : 4.1/5 (71 download)
Download or read book Partially Observed Markov Decision Processes written by Vikram Krishnamurthy and published by Cambridge University Press. This book was released on 2016-03-21 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to real-world applications in controlled sensing. Computations are kept to a minimum, enabling students and researchers in engineering, operations research, and economics to understand the methods and determine the structure of their optimal solution.
Author : Chang Eun Kim
Publisher :
ISBN 13 :
Total Pages : 140 pages
Book Rating : 4.:/5 (178 download)
Download or read book Optimal Replacement Policy for a Partially Observable Markov Decision Process Model written by Chang Eun Kim and published by . This book was released on 1986 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Edward Jay Sondik
Publisher :
ISBN 13 :
Total Pages : 218 pages
Book Rating : 4.:/5 (473 download)
Download or read book The Optimal Control of Partially Observable Markov Processes written by Edward Jay Sondik and published by . This book was released on 1971 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: The report studies the control of a finite-state, discrete-time Markov process characterized by incomplete state observation. The process is viewed through a set of outputs such that the probability of observing a given output is dependent on the current state of the Markov process. The observed stochastic process consisting of the time sequence of outputs generated by the imbedded Markov process is termed a partially observable Markov process. A finite number of alternative parameter sets for the partially observable process are available. Associated with each alternative is a set of costs for making transitions between the states of the Markov process and for producing the various outputs. At each time period an observer must select a control alternative to minimize the total expected operating costs for the process. The thesis consists of two major sections: In the first section the state of the partially observable Markov process is proved to be the vector of state occupancy probabilities for the Markov process. Using this concept of state, an algorithm is developed to solve for the optimal control as a function of a finite operating time. The algorithm produces an exact solution for the optimal control over the complete state space of a general partially observable Markov process, and is applicable to both discounted and nondiscounted problems The second section deals with the case of infinite operating time, and is subdivided into the cases of discounted and nondiscounted costs. (Author).
Author : Olivier Sigaud
Publisher : John Wiley & Sons
ISBN 13 : 1118620100
Total Pages : 367 pages
Book Rating : 4.1/5 (186 download)
Download or read book Markov Decision Processes in Artificial Intelligence written by Olivier Sigaud and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.
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Author : Pascal Poupart
Publisher : Library and Archives Canada = Bibliothèque et Archives Canada
ISBN 13 : 9780494027271
Total Pages : 288 pages
Book Rating : 4.0/5 (272 download)
Download or read book Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes [microform] written by Pascal Poupart and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in real-world problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finite-horizon discrete POMDP is PSPACE-complete. In practice, two important sources of intractability plague most solution algorithms: Large policy spaces and large state spaces. In practice, it is critical to simultaneously mitigate the impact of complex policy representations and large state spaces. Hence, this thesis describes three approaches that combine techniques capable of dealing with each source of intractability: VDC with BPI, VDC with Perseus (a randomized point-based value iteration algorithm by Spaan and Vlassis [136]), and state abstraction with Perseus. The scalability of those approaches is demonstrated on two problems with more than 33 million states: synthetic network management and a real-world system designed to assist elderly persons with cognitive deficiencies to carry out simple daily tasks such as hand-washing. This represents an important step towards the deployment of POMDP techniques in ever larger, real-world, sequential decision making problems. On the other hand, for many real-world POMDPs it is possible to define effective policies with simple rules of thumb. This suggests that we may be able to find small policies that are near optimal. This thesis first presents a Bounded Policy Iteration (BPI) algorithm to robustly find a good policy represented by a small finite state controller. Real-world POMDPs also tend to exhibit structural properties that can be exploited to mitigate the effect of large state spaces. To that effect, a value-directed compression (VDC) technique is also presented to reduce POMDP models to lower dimensional representations.
Author : A. B. Piunovskiy
Publisher : World Scientific
ISBN 13 : 1848167938
Total Pages : 308 pages
Book Rating : 4.8/5 (481 download)
Download or read book Examples in Markov Decision Processes written by A. B. Piunovskiy and published by World Scientific. This book was released on 2013 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions imposed in the theorems on Markov Decision Processes. Many of the examples are based upon examples published earlier in journal articles or textbooks while several other examples are new. The aim was to collect them together in one reference book which should be considered as a complement to existing monographs on Markov decision processes. The book is self-contained and unified in presentation. The main theoretical statements and constructions are provided, and particular examples can be read independently of others. Examples in Markov Decision Processes is an essential source of reference for mathematicians and all those who apply the optimal control theory to practical purposes. When studying or using mathematical methods, the researcher must understand what can happen if some of the conditions imposed in rigorous theorems are not satisfied. Many examples confirming the importance of such conditions were published in different journal articles which are often difficult to find. This book brings together examples based upon such sources, along with several new ones. In addition, it indicates the areas where Markov decision processes can be used. Active researchers can refer to this book on applicability of mathematical methods and theorems. It is also suitable reading for graduate and research students where they will better understand the theory.
Author : Margaret L. Brandeau
Publisher : Springer Science & Business Media
ISBN 13 : 1402080662
Total Pages : 870 pages
Book Rating : 4.4/5 (2 download)
Download or read book Operations Research and Health Care written by Margaret L. Brandeau and published by Springer Science & Business Media. This book was released on 2006-04-04 with total page 870 pages. Available in PDF, EPUB and Kindle. Book excerpt: In both rich and poor nations, public resources for health care are inadequate to meet demand. Policy makers and health care providers must determine how to provide the most effective health care to citizens using the limited resources that are available. This chapter describes current and future challenges in the delivery of health care, and outlines the role that operations research (OR) models can play in helping to solve those problems. The chapter concludes with an overview of this book – its intended audience, the areas covered, and a description of the subsequent chapters. KEY WORDS Health care delivery, Health care planning HEALTH CARE DELIVERY: PROBLEMS AND CHALLENGES 3 1.1 WORLDWIDE HEALTH: THE PAST 50 YEARS Human health has improved significantly in the last 50 years. In 1950, global life expectancy was 46 years [1]. That figure rose to 61 years by 1980 and to 67 years by 1998 [2]. Much of these gains occurred in low- and middle-income countries, and were due in large part to improved nutrition and sanitation, medical innovations, and improvements in public health infrastructure.
Author : Sheldon Ira Wolk
Publisher :
ISBN 13 :
Total Pages : 244 pages
Book Rating : 4.:/5 (212 download)
Download or read book Optimal Control of Partially Observed Markov Chains Admitting Strong and Weak Interactions written by Sheldon Ira Wolk and published by . This book was released on 1988 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Frans A. Oliehoek
Publisher : Springer
ISBN 13 : 3319289292
Total Pages : 146 pages
Book Rating : 4.3/5 (192 download)
Download or read book A Concise Introduction to Decentralized POMDPs written by Frans A. Oliehoek and published by Springer. This book was released on 2016-06-03 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.
Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 115 pages
Book Rating : 4.:/5 (661 download)
Download or read book Markov Decision Process written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-27 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Markov Decision Process A discrete-time stochastic control process is referred to as a Markov decision process (MDP) in the field of mathematics. It offers a mathematical framework for modeling decision making in scenarios in which the outcomes are partially controlled by a decision maker and partly determined by random chance. The study of optimization issues that can be handled by dynamic programming lends itself well to the use of MDPs. At the very least, MDPs were recognized to exist in the 1950s. Ronald Howard's book, published in 1960 and titled Dynamic Programming and Markov Processes, is credited for initiating a core body of study on Markov decision processes. They have applications in a wide variety of fields, including as robotics, automatic control, economics, and manufacturing, among others. Because Markov decision processes are an extension of Markov chains, the Russian mathematician Andrey Markov is where the term "Markov decision processes" (MDPs) originated. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Markov decision process Chapter 2: Markov chain Chapter 3: Reinforcement learning Chapter 4: Bellman equation Chapter 5: Admissible decision rule Chapter 6: Partially observable Markov decision process Chapter 7: Temporal difference learning Chapter 8: Multi-armed bandit Chapter 9: Optimal stopping Chapter 10: Metropolis-Hastings algorithm (II) Answering the public top questions about markov decision process. (III) Real world examples for the usage of markov decision process in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of markov decision process' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of markov decision process. What is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Author : Georgios Theocharous
Publisher :
ISBN 13 :
Total Pages : 438 pages
Book Rating : 4.3/5 (129 download)
Download or read book Hierarchical Learning and Planning in Partially Observable Markov Decision Processes written by Georgios Theocharous and published by . This book was released on 2002 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Abraham Nir
Publisher :
ISBN 13 :
Total Pages : 56 pages
Book Rating : 4.:/5 (615 download)
Download or read book Optimal Control of a Partially Observable Markov Chain written by Abraham Nir and published by . This book was released on 1986 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Nan Kong
Publisher : John Wiley & Sons
ISBN 13 : 1118960130
Total Pages : 430 pages
Book Rating : 4.1/5 (189 download)
Download or read book Decision Analytics and Optimization in Disease Prevention and Treatment written by Nan Kong and published by John Wiley & Sons. This book was released on 2018-02-02 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: A systematic review of the most current decision models and techniques for disease prevention and treatment Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making. This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment: Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.
