„... the first monograph devoted to a new interesting research area combining logic with optimization methods.“ (Mathematical Reviews, Issue 2001j)
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.
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.
