Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262029251
Format: PDF, Kindle
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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

Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262331713
Format: PDF
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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.

Decision making under uncertainty

Author: Charles A. Holloway
Publisher: Prentice Hall
ISBN:
Format: PDF, ePub
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Introduction and basic concepts; Models and probability; Choices and preferences; Preference assessment procedures; Behavioral assumptions and limitations of decision analysis; Risk sharing and incentives; Choices with multiple attributes.

Applied State Estimation and Association

Author: Chaw-Bing Chang
Publisher: MIT Press
ISBN: 0262335069
Format: PDF, ePub, Mobi
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Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications. Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes.

Decisions Under Uncertainty

Author: Ian Jordaan
Publisher: Cambridge University Press
ISBN: 9780521782777
Format: PDF, ePub, Mobi
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Risk assessment theory and practice for graduate engineering students, practising engineers, designers and project managers, first published in 2005.

Principles of Risk Analysis

Author: Charles Yoe
Publisher: CRC Press
ISBN: 1439857504
Format: PDF, ePub
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In every decision context there are things we know and things we do not know. Risk analysis uses science and the best available evidence to assess what we know—and it is intentional in the way it addresses the importance of the things we don’t know. Principles of Risk Analysis: Decision Making Under Uncertainty lays out the tasks of risk analysis in a straightforward, conceptual manner that is consistent with the risk models of all communities of practice. It answers the questions "what is risk analysis?" and "how do I do this?" Distilling the common principles of the many risk tribes and dialects into serviceable definitions and narratives, the book provides a foundation for the practice of risk analysis and decision making under uncertainty for professionals from all walks of life. In the first part of the book, readers learn the language, models, and concepts of risk analysis and its three component tasks—risk management, assessment, and communication. The second part of the book supplies the tools, techniques, and methodologies to help readers apply the principles. From problem identification and brainstorming to model building and choosing a probability distribution, the author walks readers through the how-to of risk assessment. Addressing the critical task of risk communication, he explains how to present the results of assessments and how to develop effective messages. The book’s simple and straightforward style—based on the author’s decades of experience as a risk analyst, trainer, and educator—strips away the mysterious aura that often accompanies risk analysis. It describes the principles in a manner that empowers readers to begin the practice of risk analysis, to better understand and use the models and practice of their individual fields, and to gain access to the rich and sophisticated professional literature on risk analysis. Additional exercises as well as a free student version of the Palisade Corporation DecisionTools® Suite software and files used in the preparation of this book are available for download.

Making Better Decisions

Author: Itzhak Gilboa
Publisher: John Wiley & Sons
ISBN: 1444336517
Format: PDF, Docs
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Making Better Decisions introduces readers to some of the principal aspects of decision theory, and examines how these might lead us to make better decisions. • Introduces readers to key aspects of decision theory and examines how they might help us make better decisions • Presentation of material encourages readers to imagine a situation and make a decision or a judgment • Offers a broad coverage of the subject including major insights from several sub-disciplines: microeconomic theory, decision theory, game theory, social choice, statistics, psychology, and philosophy • Explains these insights informally in a language that has minimal mathematical notation or jargon, even when describing and interpreting mathematical theorems • Critically assesses the theory presented within the text, as well as some of its critiques • Includes a web resource for teachers and students

Perspectives on Defense Systems Analysis

Author: William P. Delaney
Publisher: MIT Press
ISBN: 0262029359
Format: PDF, ePub, Docs
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A guide to defense systems analysis by experts who have worked on systems that range from air defense to space defense.

Markov Decision Processes in Artificial Intelligence

Author: Olivier Sigaud
Publisher: John Wiley & Sons
ISBN: 1118620100
Format: PDF, ePub
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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). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications.

Mathematics of Big Data

Author: Jeremy Kepner
Publisher: MIT Press
ISBN: 0262347911
Format: PDF
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The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies. Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges. The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.