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Competitive Markov Decision Processes

Author: Jerzy Filar
Publisher: Springer Science & Business Media
ISBN: 1461240549
Size: 68.14 MB
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This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to present a rig orous treatment that combines two significant research topics: Stochastic Games and Markov Decision Processes, which have been studied exten sively, and at times quite independently, by mathematicians, operations researchers, engineers, and economists. Since Markov decision processes can be viewed as a special noncompeti tive case of stochastic games, we introduce the new terminology Competi tive Markov Decision Processes that emphasizes the importance of the link between these two topics and of the properties of the underlying Markov processes. The book is designed to be used either in a classroom or for self-study by a mathematically mature reader. In the Introduction (Chapter 1) we outline a number of advanced undergraduate and graduate courses for which this book could usefully serve as a text. A characteristic feature of competitive Markov decision processes - and one that inspired our long-standing interest - is that they can serve as an "orchestra" containing the "instruments" of much of modern applied (and at times even pure) mathematics. They constitute a topic where the instruments of linear algebra, applied probability, mathematical program ming, analysis, and even algebraic geometry can be "played" sometimes solo and sometimes in harmony to produce either beautifully simple or equally beautiful, but baroque, melodies, that is, theorems.

Handbook Of Markov Decision Processes

Author: Eugene A. Feinberg
Publisher: Springer Science & Business Media
ISBN: 1461508053
Size: 72.43 MB
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Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. The goal is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation.

Markov Decision Processes In Practice

Author: Richard J. Boucherie
Publisher: Springer
ISBN: 3319477668
Size: 74.21 MB
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This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car . Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.

Markov Decision Processes With Applications To Finance

Author: Nicole Bäuerle
Publisher: Springer Science & Business Media
ISBN: 9783642183249
Size: 21.48 MB
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The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples, mostly taken from the fields of finance and operations research. By using a structural approach many technicalities (concerning measure theory) are avoided. They cover problems with finite and infinite horizons, as well as partially observable Markov decision processes, piecewise deterministic Markov decision processes and stopping problems. The book presents Markov decision processes in action and includes various state-of-the-art applications with a particular view towards finance. It is useful for upper-level undergraduates, Master's students and researchers in both applied probability and finance, and provides exercises (without solutions).

Dynamic Modelling And Control Of National Economies 1989

Author: N.M. Christodoulakis
Publisher: Elsevier
ISBN: 1483298825
Size: 54.93 MB
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The Symposium aimed at analysing and solving the various problems of representation and analysis of decision making in economic systems starting from the level of the individual firm and ending up with the complexities of international policy coordination. The papers are grouped into subject areas such as game theory, control methods, international policy coordination and the applications of artificial intelligence and experts systems as a framework in economic modelling and control. The Symposium therefore provides a wide range of important information for those involved or interested in the planning of company and national economics.

Intelligent Data Engineering And Automated Learning

Author: Jiming Liu
Publisher: Springer Science & Business Media
ISBN: 354040550X
Size: 15.34 MB
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This book constitutes the throughly refereed post-proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003, held in Hong Kong, China in March 2003. The 164 revised papers presented were carefully reviewed and selected from 321 submissions; for inclusion in this post-proceedings another round of revision was imposed. The papers are organized in topical sections an agents, automated learning, bioinformatics, data mining, multimedia information, and financial engineering.

Control Of Spatially Structured Random Processes And Random Fields With Applications

Author: Ruslan K. Chornei
Publisher: Springer Science & Business Media
ISBN: 038731279X
Size: 79.71 MB
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This book is devoted to the study and optimization of spatiotemporal stochastic processes - processes which develop simultaneously in space and time under random influences. These processes are seen to occur almost everywhere when studying the global behavior of complex systems. The book presents problems and content not considered in other books on controlled Markov processes, especially regarding controlled Markov fields on graphs.

Automata Languages And Programming

Author: Luca Aceto
Publisher: Springer Science & Business Media
ISBN: 3540705740
Size: 17.58 MB
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Annotation The two-volume set LNCS 5125 and LNCS 5126 constitutes the refereed proceedings of the 35th International Colloquium on Automata, Languages and Programming, ICALP 2008, held in Reykjavik, Iceland, in July 2008.The 126 revised full papers presented together with 4 invited lectures were carefully reviewed and selected from a total of 407 submissions. The papers are grouped in three major tracks on algorithms, automata, complexity and games, on logic, semantics, and theory of programming, and on security and cryptography foundations. LNCS 5126 contains 56 contributions of track B and track C selected from 208 submissions and 2 invited lectures. The papers for track B are organized in topical sections on bounds, distributed computation, real-time and probabilistic systems, logic and complexity, words and trees, nonstandard models of computation, reasoning about computation, and verification. The papers of track C cover topics in security and cryptography such as theory, secure computation, two-party protocols and zero-knowledge, encryption with special properties/quantum cryptography, various types of hashing, as well as public-key cryptography and authentication

Simulation Based Algorithms For Markov Decision Processes

Author: Hyeong Soo Chang
Publisher: Springer Science & Business Media
ISBN: 1447150228
Size: 77.10 MB
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Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.