Request Inspection Copy

If you are an Academic or Teacher and wish to consider this book as a prescribed textbook for your course, you may be eligible for a complimentary inspection copy. Please complete this form, including information about your position, campus and course, before adding to cart.

* Required Fields

To complete your Inspection Copy Request you will need to click the Checkout button in the right margin and complete the checkout formalities. You can include Inspection Copies and purchased items in the same shopping cart, see our Inspection Copy terms for further information.

Any Questions? Please email our text Support Team on text@footprint.com.au

Submit

Email this to a friend

* ALL required Fields

Order Inspection Copy

An inspection copy has been added to your shopping cart

Decision Making Under Uncertainty: Theory and Application

by Mykel J Kochenderfer The MIT Press
Pub Date:
07/2015
ISBN:
9780262029254
Format:
Hbk 352 pages
Price:
AU$152.00 NZ$155.65
Product Status: Temporarily Out of Stock
add to your cart
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.
Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.