Download or read book Military Stochastic Scheduling Treated As a Multi Armed Bandit Problem written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Blue airborne force attacks a region defended by a single Red surface-to-air missile system (SAM). Red is uncertain about the Blues he faces, but is able to learn about them during the engagement. Red's objective is to develop a policy for shooting at the Blues to maximize the value of Blues shot down before he himself is destroyed. We show that index policies are optimal for Red in a range of scenarios and yield effective heuristics more generally. The quality of such index heuristics is confirmed in a computational study.
Download or read book Military Stochastic Scheduling Treated As a Multi Armed Bandit Problem written by and published by . This book was released on 2001 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Blue airborne force attacks a region defended by a single Red surface-to-air missile system (SAM). Red is uncertain about the Blues he faces, but is able to learn about them during the engagement. Red's objective is to develop a policy for shooting at the Blues to maximize the value of Blues shot down before he himself is destroyed. We show that index policies are optimal for Red in a range of scenarios and yield effective heuristics more generally. The quality of such index heuristics is confirmed in a computational study.
Download or read book Restless Multi Armed Bandit in Opportunistic Scheduling written by Kehao Wang and published by Springer. This book was released on 2021-06-03 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides foundations for the understanding and design of computation-efficient algorithms and protocols for those interactions with environment, i.e., wireless communication systems. The book provides a systematic treatment of the theoretical foundation and algorithmic tools necessarily in the design of computation-efficient algorithms and protocols in stochastic scheduling. The problems addressed in the book are of both fundamental and practical importance. Target readers of the book are researchers and advanced-level engineering students interested in acquiring in-depth knowledge on the topic and on stochastic scheduling and their applications, both from theoretical and engineering perspective.
Download or read book Stochastic Scheduling with Multi armed Bandits written by Josipa Mickova and published by . This book was released on 2000 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multi armed Bandit Allocation Indices written by John Gittins and published by John Wiley & Sons. This book was released on 2011-02-18 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-armed bandit problem and his subsequent investigation of a wide of sequential resource allocation and stochastic scheduling problems. Since then there has been a remarkable flowering of new insights, generalizations and applications, to which Glazebrook and Weber have made major contributions. This second edition brings the story up to date. There are new chapters on the achievable region approach to stochastic optimization problems, the construction of performance bounds for suboptimal policies, Whittle's restless bandits, and the use of Lagrangian relaxation in the construction and evaluation of index policies. Some of the many varied proofs of the index theorem are discussed along with the insights that they provide. Many contemporary applications are surveyed, and over 150 new references are included. Over the past 40 years the Gittins index has helped theoreticians and practitioners to address a huge variety of problems within chemometrics, economics, engineering, numerical analysis, operational research, probability, statistics and website design. This new edition will be an important resource for others wishing to use this approach.
Download or read book Naval Research Logistics written by and published by . This book was released on 1987 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal Stochastic Scheduling written by Xiaoqiang Cai and published by Springer Science & Business Media. This book was released on 2014-03-20 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many interesting and important results on stochastic scheduling problems have been developed in recent years, with the aid of probability theory. This book provides a comprehensive and unified coverage of studies in stochastic scheduling. The objective is two-fold: (i) to summarize the elementary models and results in stochastic scheduling, so as to offer an entry-level reading material for students to learn and understand the fundamentals of this area and (ii) to include in details the latest developments and research topics on stochastic scheduling, so as to provide a useful reference for researchers and practitioners in this area. Optimal Stochastic Scheduling is organized into two parts: Chapters 1-4 cover fundamental models and results, whereas Chapters 5-10 elaborate on more advanced topics. More specifically, Chapter 1 provides the relevant basic theory of probability and then introduces the basic concepts and notation of stochastic scheduling. In Chapters 2 and 3, the authors review well-established models and scheduling policies, under regular and irregular performance measures, respectively. Chapter 4 describes models with stochastic machine breakdowns. Chapters 5 and 6 introduce, respectively, the optimal stopping problems and the multi-armed bandit processes, which are necessary for studies of more advanced subjects in subsequent chapters. Chapter 7 is focused on optimal dynamic policies, which allow adjustments of policies based on up-to-date information. Chapter 8 describes stochastic scheduling with incomplete information in the sense that the probability distributions of random variables contain unknown parameters, which can however be estimated progressively according to updated information. Chapter 9 is devoted to the situation where the processing time of a job depends on the time when it is started. Lastly, in Chapter 10 the authors look at several recent models beyond those surveyed in the previous chapters.
Download or read book Deterministic and Stochastic Scheduling written by M.A. Dempster and published by Springer Science & Business Media. This book was released on 1982-04-30 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the NATO Advanced Study and Research Institute on Theoretical Approaches to Scheduling Problems, Durham, England, July 6-17, 1981
Download or read book Stochastic Approximation on a Discrete Set and the Multi armed Bandit Problem written by Václav Dupač and published by . This book was released on 1981 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stochastic Control Approach to the Multi armed Bandit Problems written by Tanut Treetanthiploet and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On Multi armed Bandit in Dynamic Systems written by Keqin Liu and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-armed bandit (MAB) is a classical problem in stochastic optimization with a wide range of engineering applications. The first MAB problem was proposed in 1933 for the application of clinical trial. The problem, however, remained open for over 40 years until the breakthrough by Gittins in 1974. Under a Bayesian formulation, Gittins proved that an index policy is optimal, thus reducing the complexity of finding the optimal policy from exponential to linear with the systemsize. In 1985, Lai and Robbins established the optimal policy for the MAB under a non-Bayesian formulation. Since these milestones, MAB have attracted numerous research efforts on generalizing the associated mathematical theory and broadening the applications. In this thesis, we present our contributions to the basic theories of both the Bayesian and the non-Bayesian frameworks of MAB motivated by engineering problems in dynamic systems. Within the Bayesian framework, we address an important and still largely open extension of the classic MAB---the Restless Multi-Armed Bandit (RMAB). In 1988, Whittle generalized the classic MAB to RMAB that considers the scenario where the system dynamics cannot be directlycontrolled. This generalization significantly broadens the application area of MAB but renders Gittins index policy suboptimal. As shown by Papadimitriou and Tsitsiklis, finding the optimal solution to an RMAB is PSPACE-hard in general. Whittle proposed a heuristic index policy with linear complexity, which was shown to be asymptotically (when the system size, i.e., the number of arms, approaches infinity) optimal under certain conditions by Weber and Weiss in 1990. The difficulty of implementing Whittle index policy lies in the complexity of establishing its existence (the so-called indexability), computing the index, and establishing its optimality in the finite regime. The study of Whittle index policy often relies on numerical calculation that is infeasible for RMAB with infinite state space. In this thesis, we show that for a significant class of RMAB with an infinite state space, the indexability can be established, Whittle index can be obtained in closed-form, and, under certain conditions, achieves the optimal performance with a simple semi-universal structure that is robust against model mismatch and variations. To our best knowledge, this appears to be the first nontrivial RMAB for which Whittle index policy is proven to be optimal for a finite-size system. This class of RMAB finds a broad range of applications, from dynamic multichannel access in communication networks to bio/chemical monitoring systems, from target tracking/collecting in multi-agent systems to resource-constrained jamming/anti-jamming, from network anomaly detection to supervisory control systems. Furthermore, our approach to establishing the indexability, solving for Whittle index and characterizing its optimality is not limited to this class of RMAB and provides a set of possible techniques for analyzing the general RMAB. For the non-Bayesian framework, we extend the classic MAB that assumes a single player to the case of multiple distributed players. Players make decisions solely based on their local observationand decision histories without exchanging information. We formulate the problem as a decentralized MAB under general reward, observation, and collision models. We show that the optimal performance (measured by system regret) in the decentralized MAB achieves the same logarithmic order as that in the classic centralized MAB where players act collectively as a single entity by exchanging observations and making decisions jointly. Based on a Time Division and Fair Sharing (TDFS) structure, a general framework of constructing order-optimal and fair decentralized polices is proposed. The generality of the TDFS framework leads to its wide applications to distributed learning problems in multi-channel communication systems, multi-agent systems, web search and internet advertising, social networks, etc.
Download or read book Introduction to Multi Armed Bandits written by Aleksandrs Slivkins and published by . This book was released on 2019-10-31 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first book to provide a textbook like treatment of the subject.
Download or read book Bandit Algorithms written by Tor Lattimore and published by Cambridge University Press. This book was released on 2020-07-16 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.
Download or read book Foundations and Applications of Sensor Management written by Alfred Olivier Hero and published by Springer Science & Business Media. This book was released on 2007-10-23 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field. The editors and contributors to this book are pioneers in the area of active sensing and sensor management, and represent the diverse communities that are targeted.
Download or read book Government Reports Announcements Index written by and published by . This book was released on 1995 with total page 838 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Patterns Predictions and Actions Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
