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From nlinux.org Lincoln Laboratory Series

Decision Making Under Hesitation

Theory and also Application

By Mykel J. Kochenderfer

With Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Daviboy Reynolds, Jachild R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre and also John Vian


An introduction to decision making under uncertainty from a computational perspective, covering both theory and also applications varying from speech recognition to airborne collision avoidance.
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This book is a tour de force for its methodical therapy of the latest advances in decision making and also planning under uncertainty. The in-depth discussion on modeling worries and computational efficiency within real-human being applications makes it inuseful for students and practitioners afavor.

David Hsu

Professor of Computer Science, National University of Singapore


An introduction to decision making under uncertainty from a computational perspective, extending both concept and also applications ranging from speech recognition to airborne collision avoidance.

Many type of necessary problems involve decision making under uncertainty—that is, choosing actions based on regularly imperfect observations, via unrecognized outcomes. Designers of automated decision assistance units have to take into account the miscellaneous resources of uncertainty while balancing the multiple missions of the system. This book gives an arrival to the challenges of decision making under uncertainty from a computational perspective. It presents both the concept behind decision making models and algorithms and also a collection of example applications that range from speech acknowledgment to aircraft collision avoidance.

Focutilizing on two methods for designing decision agents, planning and also reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical version that captures probabilistic relationships in between variables; utility concept as a frame for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and participating decision making entailing multiple interacting agents. A series of applications reflects how the theoretical principles can be applied to devices for attribute-based perboy search, speech applications, collision avoidance, and unmanned aircraft persistent security.

Decision Making Under Suspicion unifies study from various neighborhoods using regular notation, and also is easily accessible to students and also researchers across engineering disciplines that have some prior exposure to probcapability concept and calculus. It can be offered as a message for advanced undergraduate and graduate students in fields consisting of computer scientific research, aerospace and also electrical engineering, and administration science. It will certainly additionally be a beneficial experienced referral for researchers in a range of disciplines.


Instructor Resources

Downloadable instructor resources available for this title: exams with solutions, slides, and also code examples


Hardcover $80.00 X ISBN: 9780262029254 352 pp. | 7 in x 9 in 19 color illus., 72 b&w illus. July 2015

Authors

Mykel J. Kochenderfer Mykel Kochenderfer is Associate Professor at Stanford College, where he is Director of the Stanford Intelligent Systems Laboratory (SISL). He is the writer of Decision Making Under Skepticism (nlinux.org Press). Kochenderfer and Tim Wheeler are coauthors of Algorithms for Optimization (nlinux.org Press).

Contributors

Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davikid Reynolds, Jachild R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre, and John Vian.


Endorsements

This book is a tour de force for its organized therapy of the latest breakthroughs in decision making and also planning under uncertainty. The comprehensive discussion on modeling worries and also computational effectiveness within real-world applications renders it inhandy for students and practitioners afavor.

David Hsu

Professor of Computer Science, National University of Singapore

This book is a thoturbulent and also authoritative treatment of the mathematics of planning and also reasoning under uncertainty. The real-life instance researches that finish the book aid ground the concept via concrete examples that have the right to serve as models for researchers emerging brand-new applications of these effective principles. It would make an excellent message for a semester-long course on the subject of algorithmic decision making.

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Michael L. Littman

Professor of Computer Science, Brvery own University

An intuitive and obtainable development to the amazing topic of decision making under uncertainty—very timely given the latest developments in robotics and autonomous devices. Problems are framed in the probabilistic inference formulation and also provide a modern-day take on the classical reinforcement learning paradigm under partial observcapacity, through organic links to real-world applications.