Optimization Methods for Logical Inference
Rights Contact Login For More Details
More About This Title Optimization Methods for Logical Inference
English
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.
English
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.
JOHN N. HOOKER is a professor in the Graduate School of Industrial Administration at Carnegie Mellon University.
English
Propositional Logic: Special Cases.
Propositional Logic: The General Case.
Probabilistic and Related Logics.
Predicate Logic.
Nonclassical and Many-Valued Logics.
Appendix.
Bibliography.
Index.
Propositional Logic: The General Case.
Probabilistic and Related Logics.
Predicate Logic.
Nonclassical and Many-Valued Logics.
Appendix.
Bibliography.
Index.
English
"...the first monograph devoted to a new interesting research area combining logic with optimization methods." (Mathematical Reviews, Issue 2001j)
