Download or read book Parameter Estimation for Partially Observable Markov Decision Processes written by Srinivas S. Kambhamettu and published by . This book was released on 2000 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation and Adaptative Decision Making for Partially Observable Markov Systems written by David Tobia Signori and published by . This book was released on 1968 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bounded parameter Partially Observable Markov Decision Processes written by 倪耀東 and published by . This book was released on 2009 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

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.

Download or read book Partially Observable Markov Decision Process written by Gerard Blokdyk and published by Createspace Independent Publishing Platform. This book was released on 2018-05-29 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Which customers cant participate in our Partially observable Markov decision process domain because they lack skills, wealth, or convenient access to existing solutions? Can we add value to the current Partially observable Markov decision process decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)? Who are the people involved in developing and implementing Partially observable Markov decision process? How does Partially observable Markov decision process integrate with other business initiatives? Does the Partially observable Markov decision process performance meet the customer's requirements? This premium Partially observable Markov decision process self-assessment will make you the assured Partially observable Markov decision process domain master by revealing just what you need to know to be fluent and ready for any Partially observable Markov decision process challenge. How do I reduce the effort in the Partially observable Markov decision process work to be done to get problems solved? How can I ensure that plans of action include every Partially observable Markov decision process task and that every Partially observable Markov decision process outcome is in place? How will I save time investigating strategic and tactical options and ensuring Partially observable Markov decision process costs are low? How can I deliver tailored Partially observable Markov decision process advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Partially observable Markov decision process essentials are covered, from every angle: the Partially observable Markov decision process self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Partially observable Markov decision process outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Partially observable Markov decision process practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Partially observable Markov decision process are maximized with professional results. Your purchase includes access details to the Partially observable Markov decision process self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book.

Download or read book Learning in Partially Observable Markov Decision Processes written by Mohit Sachan and published by . This book was released on 2012 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need to address a number of realistic problems. A number of methods exist for learning in POMDPs, but learning with limited amount of information about the model of POMDP remains a highly anticipated feature. Learning with minimal information is desirable in complex systems as methods requiring complete information among decision makers are impractical in complex systems due to increase of problem dimensionality. In this thesis we address the problem of decentralized control of POMDPs with unknown transition probabilities and reward. We suggest learning in POMDP using a tree based approach. States of the POMDP are guessed using this tree. Each node in the tree has an automaton in it and acts as a decentralized decision maker for the POMDP. The start state of POMDP is known as the landmark state. Each automaton in the tree uses a simple learning scheme to update its action choice and requires minimal information. The principal result derived is that, without proper knowledge of transition probabilities and rewards, the automata tree of decision makers will converge to a set of actions that maximizes the long term expected reward per unit time obtained by the system. The analysis is based on learning in sequential stochastic games and properties of ergodic Markov chains. Simulation results are presented to compare the long term rewards of the system under different decision control algorithms.

Download or read book Robust Partially Observable Markov Decision Processes written by Mohammad Rasouli and published by . This book was released on 2018 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a variety of applications, decisions needs to be made dynamically after receiving imperfect observations about the state of an underlying system. Partially Observable Markov Decision Processes (POMDPs) are widely used in such applications. To use a POMDP, however, a decision-maker must have access to reliable estimations of core state and observation transition probabilities under each possible state and action pair. This is often challenging mainly due to lack of ample data, especially when some actions are not taken frequently enough in practice. This significantly limits the application of POMDPs in real-world settings. In healthcare, for example, medical tests are typically subject to false-positive and false-negative errors, and hence, the decision-maker has imperfect information about the health state of a patient. Furthermore, since some treatment options have not been recommended or explored in the past, data cannot be used to reliably estimate all the required transition probabilities regarding the health state of the patient. We introduce an extension of POMDPs, termed Robust POMDPs (RPOMDPs), which allows dynamic decision-making when there is ambiguity regarding transition probabilities. This extension enables making robust decisions by reducing the reliance on a single probabilistic model of transitions, while still allowing for imperfect state observations. We develop dynamic programming equations for solving RPOMDPs, provide a sufficient statistic and an information state, discuss ways in which their computational complexity can be reduced, and connect them to stochastic zero-sum games with imperfect private monitoring.

Download or read book Incorporating Expert Judgement Into Condition Based Maintenance Decision Support Using a Coupled Hidden Markov Model and a Partially Observable Markov Decision Process written by Samaneh Balali and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Preventive maintenance consists of activities performed to maintain a system in a satisfactory functional condition. Condition Based Maintenance (CBM) aims to reduce the cost of preventive maintenance by supporting decisions on performing maintenance actions, based on information reflecting a system's health condition. In practice, the condition related information can be obtained in various ways, including continuous condition monitoring performed by sensors, or subjective assessment performed by humans. An experienced engineer might provide such subjective assessment by visually inspecting a system, or by interpreting the data collected by condition monitoring devices, and hence give an "expert judgement" on the state of the system. There is limited academic literature on the development of CBM models incorporating expert judgement. This research aims to reduce this gap by developing models that formally incorporate expert judgement into the CBM decision process. A Coupled Hidden Markov Model is proposed to model the evolutionary relationship between expert judgement and the true deterioration state of a system. This model is used to estimate the underlying condition of the system and predict the remaining time to failure. A training algorithm is developed to support model parameter estimation. The algorithm's performance is evaluated with respect to the number of expert judgements and initial settings of model parameters. A decision-making problem is formulated to account for the use of expert judgement in selecting maintenance actions in light of the physical investigation of the system's condition. A Partially Observable Markov Decision Process is proposed to recommend the most cost-effective decisions on inspection choice and maintenance action in two consecutive steps. An approximate method is developed to solve the proposed decision optimisation model and obtain the optimal policy. The sensitivity of the optimal policy is evaluated with respect to model parameters settings, such as the accuracy of the expert judgement.

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.

Download or read book Learning and Solving Partially Observable Markov Decision Processes written by Guy Shani and published by . This book was released on 2007 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computer and Information Sciences ISCIS 2005 written by Pinar Yolum and published by Springer Science & Business Media. This book was released on 2005-10-17 with total page 992 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 20th International Symposium on Computer and Information Sciences, ISCIS 2005, held in Istanbul, Turkey in October 2005. The 92 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 491 submissions. The papers are organized in topical sections on computer networks, sensor and satellite networks, security and cryptography, performance evaluation, e-commerce and Web services, multiagent systems, machine learning, information retrieval and natural language processing, image and speech processing, algorithms and database systems, as well as theory of computing.

Download or read book An Introduction to Natural Computation written by Dana H. Ballard and published by MIT Press. This book was released on 1999-01-22 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It is now clear that the brain is unlikely to be understood without recourse to computational theories. The theme of An Introduction to Natural Computation is that ideas from diverse areas such as neuroscience, information theory, and optimization theory have recently been extended in ways that make them useful for describing the brains programs. This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It stresses the broad spectrum of learning models—ranging from neural network learning through reinforcement learning to genetic learning—and situates the various models in their appropriate neural context. To write about models of the brain before the brain is fully understood is a delicate matter. Very detailed models of the neural circuitry risk losing track of the task the brain is trying to solve. At the other extreme, models that represent cognitive constructs can be so abstract that they lose all relationship to neurobiology. An Introduction to Natural Computation takes the middle ground and stresses the computational task while staying near the neurobiology.

Download or read book Adaptive Markov Control Processes written by Onesimo Hernandez-Lerma and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with a class of discrete-time stochastic control processes known as controlled Markov processes (CMP's), also known as Markov decision processes or Markov dynamic programs. Starting in the mid-1950swith Richard Bellman, many contributions to CMP's have been made, and applications to engineering, statistics and operations research, among other areas, have also been developed. The purpose of this book is to present some recent developments on the theory of adaptive CMP's, i. e. , CMP's that depend on unknown parameters. Thus at each decision time, the controller or decision-maker must estimate the true parameter values, and then adapt the control actions to the estimated values. We do not intend to describe all aspects of stochastic adaptive control; rather, the selection of material reflects our own research interests. The prerequisite for this book is a knowledgeof real analysis and prob ability theory at the level of, say, Ash (1972) or Royden (1968), but no previous knowledge of control or decision processes is required. The pre sentation, on the other hand, is meant to beself-contained,in the sensethat whenever a result from analysisor probability is used, it is usually stated in full and references are supplied for further discussion, if necessary. Several appendices are provided for this purpose. The material is divided into six chapters. Chapter 1 contains the basic definitions about the stochastic control problems we are interested in; a brief description of some applications is also provided.

Download or read book Markov Decision Processes with Their Applications written by Qiying Hu and published by Springer. This book was released on 2010-11-19 with total page 0 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.

Download or read book From Little s Law to Marketing Science written by John R. Hauser and published by MIT Press. This book was released on 2016-01-15 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: The legacy of a pioneer in operations research and marketing science.

Download or read book Parameter Estimation in Reliability and Life Span Models written by A Clifford Cohen and published by CRC Press. This book was released on 2020-07-26 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers an applications-oriented treatment of parameter estimation from both complete and censored samples; contains notations, simplified formats for estimates, graphical techniques, and numerous tables and charts allowing users to calculate estimates and analyze sample data quickly and easily. Anno