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1.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; Mykel J. Kochenderfer
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Cherry_Books
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Description du livre État : New. Brand New Book. N° de réf. du libraire 0262029251SRB

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2.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; Mykel J. Kochenderfer
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre État : New. N° de réf. du libraire 23749751-n

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3.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; 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. État : New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service!. N° de réf. du libraire ABE_book_new_0262029251

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4.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; 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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5.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; 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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6.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; Mykel J. Kochenderfer
Edité par MIT Press Ltd, United States (2015)
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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The Book Depository
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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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7.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; Mykel J. Kochenderfer
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Description du livre État : New. Bookseller Inventory # ST0262029251. N° de réf. du libraire ST0262029251

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8.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; Mykel J. Kochenderfer
Edité par MIT Press Ltd
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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THE SAINT BOOKSTORE
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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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9.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; Mykel J. Kochenderfer
Edité par MIT Press Ltd 2015-08-11, Cambridge, Mass. (2015)
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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Blackwell's
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Description du livre MIT Press Ltd 2015-08-11, Cambridge, Mass., 2015. hardback. État : New. N° de réf. du libraire 9780262029254

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10.

Christopher Amato; Girish Chowdhary; Hayley J. Davis Reynolds; Jonathan P. How; Mykel J. Kochenderfer
Edité par MIT Press
ISBN 10 : 0262029251 ISBN 13 : 9780262029254
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INDOO
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Description du livre MIT Press. État : New. Brand New. N° de réf. du libraire 0262029251

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