Decision Making Under Uncertainty

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

What Every Engineer Should Know About Decision Making Under Uncertainty

Author: John X. Wang
Publisher: CRC Press
ISBN: 9780203910757
Format: PDF, ePub, Docs
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Covering the prediction of outcomes for engineering decisions through regression analysis, this succinct and practical reference presents statistical reasoning and interpretational techniques to aid in the decision making process when faced with engineering problems. The author emphasizes the use of spreadsheet simulations and decision trees as important tools in the practical application of decision making analyses and models to improve real-world engineering operations. He offers insight into the realities of high-stakes engineering decision making in the investigative and corporate sectors by optimizing engineering decision variables to maximize payoff.

Decision making under uncertainty

Author: Charles A. Holloway
Publisher: Prentice Hall
ISBN:
Format: PDF
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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.

Decisions Under Uncertainty

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

Decision Theory with a Human Face

Author: Richard Bradley
Publisher: Cambridge University Press
ISBN: 1107003210
Format: PDF
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Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.

Making Better Decisions

Author: Itzhak Gilboa
Publisher: John Wiley & Sons
ISBN: 1444336517
Format: PDF
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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

Planning with Markov Decision Processes

Author: Mausam
Publisher: Morgan & Claypool Publishers
ISBN: 1608458865
Format: PDF, Docs
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Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

Uncertainty Analysis in Engineering and Sciences Fuzzy Logic Statistics and Neural Network Approach

Author: Bilal M. Ayyub
Publisher: Springer Science & Business Media
ISBN: 146155473X
Format: PDF, ePub, Mobi
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Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.