optimization methods logical inference de chandru vijay (18 résultats)

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Buch. Etat : Neu. Neuware - Neben der Mathematik entwickelte sich die Logik in letzter Zeit zu einem grundlegenden Hilfsmittel der Optimierung. Dieser Band erläutert Spezialisten und fortgeschrittenen Studenten die Ähnlichkeiten zwischen logischen und Optimierungsproblemen und zeigt, wie sich Folgerungsprobleme einfacher lösen lassen als mit den traditionellen Methoden der künstlichen Intelligenz. (05/99).

Hardcover. Etat : new. Hardcover. Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . 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." Presenting powerful, proven optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering. Logic has recently become a basic modelling tool alongside mathematics, and the two styles of modelling are beginning to combine. This volume exploits the similarity of certain logical and optimization problems to solve inference problems more rapidly than with traditional AI techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Etat : New.

Hardcover. Etat : Brand New. 1st edition. 365 pages. 9.75x6.75x1.00 inches. In Stock.

Hardcover. Etat : new. Hardcover. Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . 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." Presenting powerful, proven optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering. Logic has recently become a basic modelling tool alongside mathematics, and the two styles of modelling are beginning to combine. This volume exploits the similarity of certain logical and optimization problems to solve inference problems more rapidly than with traditional AI techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Etat : new. Hardcover. Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . 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." Presenting powerful, proven optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering. Logic has recently become a basic modelling tool alongside mathematics, and the two styles of modelling are beginning to combine. This volume exploits the similarity of certain logical and optimization problems to solve inference problems more rapidly than with traditional AI techniques. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Etat : New. Logic has recently become a basic modelling tool alongside mathematics, and the two styles of modelling are beginning to combine. This volume exploits the similarity of certain logical and optimization problems to solve inference problems more rapidly than with traditional AI techniques. Series: Wiley-Interscience Series in Discrete Mathematics and Optimization. Num Pages: 365 pages, Illustrations. BIC Classification: PBCD; PBK. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 243 x 160 x 25. Weight in Grams: 654. . 1999. 1st Edition. Hardcover. . . . .

Etat : New. Logic has recently become a basic modelling tool alongside mathematics, and the two styles of modelling are beginning to combine. This volume exploits the similarity of certain logical and optimization problems to solve inference problems more rapidly than with traditional AI techniques. Series: Wiley-Interscience Series in Discrete Mathematics and Optimization. Num Pages: 365 pages, Illustrations. BIC Classification: PBCD; PBK. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 243 x 160 x 25. Weight in Grams: 654. . 1999. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Etat : Brand New. 1st edition. 365 pages. 9.75x6.75x1.00 inches. In Stock. This item is printed on demand.