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Optimization Methods for Logical Inference
By Vijay Chandru and John Hooker
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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.
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.
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Titles in the series (19)
Combinatorial Geometry Rating: 5 out of 5 stars5/5Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction Rating: 0 out of 5 stars0 ratingsCombinatorial Optimization Rating: 5 out of 5 stars5/5Graph Theory Rating: 3 out of 5 stars3/5Optimization Methods for Logical Inference Rating: 0 out of 5 stars0 ratingsRandom Graphs Rating: 5 out of 5 stars5/5Interior Point Algorithms: Theory and Analysis Rating: 0 out of 5 stars0 ratingsGraph Coloring Problems Rating: 0 out of 5 stars0 ratingsIntroduction to the Theory of Error-Correcting Codes Rating: 0 out of 5 stars0 ratingsIntroduction to Stochastic Search and Optimization: Estimation, Simulation, and Control Rating: 4 out of 5 stars4/5Average Case Analysis of Algorithms on Sequences Rating: 0 out of 5 stars0 ratingsMaxima and Minima with Applications: Practical Optimization and Duality Rating: 0 out of 5 stars0 ratingsIntroduction to Discrete Dynamical Systems and Chaos Rating: 0 out of 5 stars0 ratingsSorting: A Distribution Theory Rating: 0 out of 5 stars0 ratingsCombinatorics Rating: 0 out of 5 stars0 ratingsCryptography, Information Theory, and Error-Correction: A Handbook for the 21st Century Rating: 0 out of 5 stars0 ratingsAn Introduction to Optimization Rating: 0 out of 5 stars0 ratingsTheory of Computational Complexity Rating: 0 out of 5 stars0 ratings
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Optimization Methods for Logical Inference - Vijay Chandru
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