Optimization Methods for Logical Inference

Vijay Chandru

ISBN 10: 0471570354 / ISBN 13: 9780471570356

Published by Wiley Mär 1999, 1999

About this Item

Neuware - 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.; 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).

Seller Inventory # 9780471570356

Bibliographic Details

• Title: Optimization Methods for Logical Inference
• Publisher: Wiley Mär 1999
• Publication Date: 1999
• Language: English
• ISBN 10: 0471570354
• ISBN 13: 9780471570356
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 25D millimeters 240 millimeters width by 161 millimeters height by 25 millimeters depth
• Weight: 742 grams

About this title

Synopsis

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.

About the Author

VIJAY CHANDRU is a professor in the Computer Science and Automation Department at the Indian Institute of Science in Bangalore, India.

JOHN N. HOOKER is a professor in the Graduate School of Industrial Administration at Carnegie Mellon University.