9783319042794-3319042793-Constraint Programming and Decision Making (Studies in Computational Intelligence, 539)

Constraint Programming and Decision Making (Studies in Computational Intelligence, 539)

ISBN-13: 9783319042794
ISBN-10: 3319042793
Edition: 2014
Author: Vladik Kreinovich, Martine Ceberio
Publication date: 2014
Publisher: Springer
Format: Hardcover 221 pages
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Book details

ISBN-13: 9783319042794
ISBN-10: 3319042793
Edition: 2014
Author: Vladik Kreinovich, Martine Ceberio
Publication date: 2014
Publisher: Springer
Format: Hardcover 221 pages

Summary

Constraint Programming and Decision Making (Studies in Computational Intelligence, 539) (ISBN-13: 9783319042794 and ISBN-10: 3319042793), written by authors Vladik Kreinovich, Martine Ceberio, was published by Springer in 2014. With an overall rating of 4.0 stars, it's a notable title among other books. You can easily purchase or rent Constraint Programming and Decision Making (Studies in Computational Intelligence, 539) (Hardcover) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

In many application areas, it is necessary to make effective decisions under constraints. Several area-specific techniques are known for such decision problems; however, because these techniques are area-specific, it is not easy to apply each technique to other applications areas. Cross-fertilization between different application areas is one of the main objectives of the annual International Workshops on Constraint Programming and Decision Making. Those workshops, held in the US (El Paso, Texas), in Europe (Lyon, France) and in Asia (Novosibirsk, Russia), from 2008 to 2012, have attracted researchers and practitioners from all over the world. This volume presents extended versions of selected papers from those workshops. These papers deal with all stages of decision making under constraints: (1) formulating the problem of multi-criteria decision making in precise terms, (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms and making these algorithms as efficient as possible and (4) taking into account interval, probabilistic and fuzzy uncertainty inherent in the corresponding decision making problems. The resulting application areas include environmental studies (selecting the best location for a meteorological tower), biology (selecting the most probable evolution history of a species), and engineering (designing the best control for a magnetic levitation train).

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