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Algorithms Decision Making by Kochenderfer Mykel (34 results)
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Condition: New. pp. 700.
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Algorithms for Decision Making
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Hardcover. Condition: new. Hardcover. A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Add to basketHardcover. Condition: Brand New. 704 pages. 9.00x8.00x1.50 inches. In Stock.
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Algorithms for Decision Making
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Algorithms for Decision Making
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Algorithms for Decision Making
Language: English
Published by MIT Press 2022-08-16, 2022
ISBN 10: 0262047012 ISBN 13: 9780262047012
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Algorithms for Decision Making
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Algorithms for Decision Making
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Algorithms for Decision Making
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Algorithms for Decision Making
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Add to basketHardcover. Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Add to basketHardcover. Condition: Brand New. 704 pages. 9.00x8.00x1.50 inches. In Stock.
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Hardcover. Condition: new. Hardcover. A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
