Decision Making Under Uncertainty ? Theory and Application

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9780262029254: Decision Making Under Uncertainty ? Theory and Application

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Kochenderfer, Mykel J.
Edité par MIT Press
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre MIT Press. État : Brand New. Ships SAME or NEXT business day. We Ship to APO/FPO addr. Choose EXPEDITED shipping and receive in 2-5 business days within the United States. See our member profile for customer support contact info. We have an easy return policy. N° de réf. du libraire 43386185

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Mykel J. Kochenderfer; Christopher Amato; Girish Chowdhary; Jonathan P. How; Hayley J. Davis Reynolds
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Description du livre État : New. N° de réf. du libraire 23749751-n

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Mykel J. Kochenderfer
Edité par MIT Press (2015)
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre MIT Press, 2015. HRD. État : New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. N° de réf. du libraire WM-9780262029254

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Kochenderfer, Mykel J.
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre 2015. HRD. État : New. New Book. Shipped from US within 10 to 14 business days. Established seller since 2000. N° de réf. du libraire TM-9780262029254

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5.

Mykel J. Kochenderfer
Edité par MIT Press Ltd, United States (2015)
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre MIT Press Ltd, United States, 2015. Hardback. État : New. 231 x 180 mm. Language: English . Brand New Book. 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. N° de réf. du libraire AAH9780262029254

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Vian, John
Edité par MIT Press
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre MIT Press. État : New. Brand New. N° de réf. du libraire 0262029251

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Mykel J. Kochenderfer
Edité par MIT Press Ltd, United States (2015)
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre MIT Press Ltd, United States, 2015. Hardback. État : New. 231 x 180 mm. Language: English . Brand New Book. 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. N° de réf. du libraire AAH9780262029254

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Mykel J. Kochenderfer
Edité par The MIT Press (2015)
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre The MIT Press, 2015. Hardcover. État : New. book. N° de réf. du libraire 0262029251

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Mykel J. Kochenderfer, Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds
Edité par MIT Press Ltd
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre MIT Press Ltd. Hardback. État : new. BRAND NEW, Decision Making Under Uncertainty: Theory and Application, Mykel J. Kochenderfer, Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, 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. N° de réf. du libraire B9780262029254

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Mykel J. Kochenderfer; Christopher Amato; Girish Chowdhary; Jonathan P. How; Hayley J. Davis Reynolds
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre État : New. Depending on your location, this item may ship from the US or UK. N° de réf. du libraire 97802620292540000000

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