The Efficiency of Theorem Proving Strategies: A Comparative and Asymptotic Analysis (Computational Intelligence)

This book is unique in that it gives asymptotic bounds on the sizes of the search spaces generated by many common theorem proving strategies. Thus it permits one to gain a theoretical understanding of the efficiencies of many different theorem proving methods. This is a fundamental new tool in the comparative study of theorem proving strategies. Written for students and researchers in computational intelligence, mathematical logic, and automated theorem proving. AUTHORBI0:Since 1984 David A. Plaisted is professor in the Department of Computer Science at the University of North Carolina. His current interests include theorem proving, term-rewriting systems and logic and functional programming. Yunshan Zhu also is a member of the Department of Computer Science at the University of North Carolina. Zhu's current research interests include theorem proving, formal methods and logical approaches of Artificial Intelligence.

A Comparative and Asymptotic Analysis