Decision Making Under Uncertainty: Theory and Application

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Mykel J. Kochenderfer (Stanford University) Christopher Amato (University of New Hampshire)

Decision Making Under Uncertainty

Theory and Application

Mykel J. Kochenderfer (Stanford University), Christopher Amato (University of New Hampshire), Girish Chowdhary , Jonathan P. How (Massachusetts Institute of Technology)

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English

Massachusetts Inst of Tec
17 July 2015

Mathematics & Sciences; Applied mathematics; Automatic control engineering; Machine learning

Series: Decision Making Under Uncertainty

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.

By: Mykel J. Kochenderfer (Stanford University)
With: Christopher Amato (University of New Hampshire), Girish Chowdhary, Jonathan P. How (Massachusetts Institute of Technology), Hayley J. Davison Reynolds (MIT Lincoln Laboratory)
Imprint: Massachusetts Inst of Tec
Country of Publication: United States
Dimensions: Height: 229mm, Width: 178mm, Spine: 21mm
Weight: 816g
ISBN: 9780262029254
ISBN 10: 0262029251
Series: Decision Making Under Uncertainty
Pages: 352
Publication Date: 17 July 2015
Recommended Age: From 18 years
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University. He is a consultant for the MIT Lincoln Laboratory.