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ePub Optimization Methods for Logical Inference download

by John Hooker,Vijay Chandru

ePub Optimization Methods for Logical Inference download
Author:
John Hooker,Vijay Chandru
ISBN13:
978-0471570356
ISBN:
0471570354
Language:
Publisher:
Wiley-Interscience; 1 edition (March 16, 1999)
Category:
Subcategory:
Management & Leadership
ePub file:
1105 kb
Fb2 file:
1427 kb
Other formats:
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Rating:
4.7
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519

Электронная книга "Optimization Methods for Logical Inference", Vijay Chandru, John Hooker

Электронная книга "Optimization Methods for Logical Inference", Vijay Chandru, John Hooker. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Optimization Methods for Logical Inference" для чтения в офлайн-режиме.

Vijay Chandru, John Hooker. Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference?

Vijay Chandru, John Hooker. it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs.

And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks.

Combinatorial optimization Logic, Symbolic and mathematical. Similar books and articles. Added to PP index 2015-02-13. No categories specified (categorize this paper).

He has co-authored the book Optimization Methods for Logical Inference, published by Wiley Interscience in 1999 ^ Vijay Chandru; John Hooker (26 September 2011). Optimization Methods for Logical Inference. John Wiley & Sons.

He has co-authored the book Optimization Methods for Logical Inference, published by Wiley Interscience in 1999. He is also a founder of the Association of Biotech led Enterprises (ABLE) and continues to serve as an executive council member. He is one of the inventors of the Simputer. Professor Chandru was elected as a fellow of the Indian Academy of Sciences in 1996 and of the Indian National Academy of Engineers in 2010. Vijay Chandru; John Hooker (26 September 2011). ISBN 978-1-118-03141-4.

Optimization methods for logical inference. V Chandru, JN Hooker. Logic-based methods for optimization. Wiley-Interscience, New York, 1999. Logic, optimization, and constraint programming. INFORMS Journal on Computing 14 (4), 295-321, 2002. International Workshop on Principles and Practice of Constraint Programmin. 1994. Optimal driving for single-vehicle fuel economy. The class of Horn clause sets in propositional logic is extended to a larger class for which the satisfiability problem can still be solved by unit resolution in linear time. J oper res SOC. Vijay Chandru.

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Logic has recently become a basic modelling tool alongside mathematics, and the two styles of modelling are beginning to combine. Thus the need for logical inference models, particularly those that involve quantitative methods, is growing.

John Hooker has published two earlier books on the methodologies of Optimization and Constraint Programming: Optimization Methods for Logical Inference (Wiley 1999) and Logic Based Methods for Optimization: Combining Optimization and Constraints Satisfaction (Wiley.

John Hooker has published two earlier books on the methodologies of Optimization and Constraint Programming: Optimization Methods for Logical Inference (Wiley 1999) and Logic Based Methods for Optimization: Combining Optimization and Constraints Satisfaction (Wiley 2000).

Merging logic and mathematics in deductive inference-an innovative,cutting-edge approach.Optimization methods for logical inference? Absolutely, say VijayChandru and John Hooker, two major contributors to this rapidlyexpanding field. And even though "solving logical inferenceproblems with optimization methods may seem a bit like eatingsauerkraut with chopsticks. . . it is the mathematical structure ofa problem that determines whether an optimization model can helpsolve it, not the context in which the problem occurs."Presenting powerful, proven optimization techniques for logicinference problems, Chandru and Hooker show how optimization modelscan be used not only to solve problems in artificial intelligenceand mathematical programming, but also have tremendous applicationin complex systems in general. They survey most of the recentresearch from the past decade in logic/optimization interfaces,incorporate some of their own results, and emphasize the types oflogic most receptive to optimization methods-propositional logic,first order predicate logic, probabilistic and related logics,logics that combine evidence such as Dempster-Shafer theory, rulesystems with confidence factors, and constraint logic programmingsystems.Requiring no background in logic and clearly explaining all topicsfrom the ground up, Optimization Methods for Logical Inference isan invaluable guide for scientists and students in diverse fields,including operations research, computer science, artificialintelligence, decision support systems, and engineering.