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Algorithms for Decision Making

Af: Tim A. Wheeler, Mykel J. Kochenderfer Engelsk Hardback
SPAR
kr 292

Algorithms for Decision Making

Af: Tim A. Wheeler, Mykel J. Kochenderfer Engelsk Hardback
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.

Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.
 
The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
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A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.

Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.
 
The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Produktdetaljer
Sprog: Engelsk
Sider: 704
ISBN-13: 9780262047012
Indbinding: Hardback
Udgave:
ISBN-10: 0262047012
Udg. Dato: 16 aug 2022
Længde: 39mm
Bredde: 211mm
Højde: 237mm
Forlag: MIT Press Ltd
Oplagsdato: 16 aug 2022
Forfatter(e) Tim A. Wheeler, Mykel J. Kochenderfer


Kategori Programmering / softwareudvikling


Sprog Engelsk


Indbinding Hardback


Sider 704


Udgave


Længde 39mm


Bredde 211mm


Højde 237mm


Udg. Dato 16 aug 2022


Oplagsdato 16 aug 2022

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