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.

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.

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.

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." —Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." —Journal of the American Statistical Association

This two-volume set of texts explores the central facts and ideas of stochastic processes, illustrating their use in models based on applied and theoretical investigations. They demonstrate the interdependence of three areas of study that usually receive separate treatments: stochastic processes, operating characteristics of stochastic systems, and stochastic optimization. Comprehensive in its scope, they emphasize the practical importance, intellectual stimulation, and mathematical elegance of stochastic models and are intended primarily as graduate-level texts.

In the fall of 1992 a conference honoring Elwood S. Buffa was held at the Anderson Graduate School of Management of the University of California, Los Angeles. This book is a collection of the work presented at that conference. The scholars who gathered to honor El are the prominent researchers in the field of Operations Management. Their collective work published in this book represents the richness of the field and provides the reader with valuable insights into its important issues and problems. While any grouping of the articles by these distinguished scholars will be arbitrary, I have organized the book in four sections. In the first section the articles dealing with the strategic issues in Operations Management are compiled. The articles deal with continuous improvement, quality, services, supply chain management, and creating value through operations. The articles that explore the interface of Operations Management with other functional areas, e.g. engineering and marketing, are grouped in the second section. The third section of the book contains articles that attempt to model some important planning problems that arise in the management of production and operations. Some of the papers in this section provide state of the art reviews of selected topic areas. Finally, the fourth section contains articles that deal with future directions for Operations Management. The authors offer several insights into the future evolution of the field. The book begins with the keynote address given by El Buffa at the start of the conference on November 2, 1991.

The 2004 Asian International Workshop on Advanced Reliability Modeling is a symposium for the dissemination of state-of-the-art research and the presentation of practice in reliability engineering and related issues in Asia. It brings together researchers, scientists and practitioners from Asian countries to discuss the state of research and practice in dealing with reliability issues at the system design (modeling) level, and to jointly formulate an agenda for future research in this engineering area. The proceedings cover all the key topics in reliability, maintainability and safety engineering, providing an in-depth presentation of theory and practice.The proceedings have been selected for coverage in:• Index to Scientific & Technical Proceedings® (ISTP® / ISI Proceedings)• Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)• CC Proceedings — Engineering & Physical Sciences