Book Description
Logic and its components (propositional, firstorder, nonclassical) play a key role in Computer Science and Artificial Intelligence. While a large amount of information exists scattered throughout various media (books, journal articles, webpages, etc.), the diffuse nature of these sources is problematic and logic as a topic benefits from a unified approach. Logic for Computer Science and Artificial Intelligence utilizes this format, surveying the tableaux, resolution, Davis and Putnam methods, logic programming, as well as for example unification and subsumption. For nonclassical logics, the translation method is detailed.
Logic for Computer Science and Artificial Intelligence is the classroomtested result of several years of teaching at Grenoble INP (Ensimag). It is conceived to allow selfinstruction for a beginner with basic knowledge in Mathematics and Computer Science, but is also highly suitable for use in traditional courses. The reader is guided by clearly motivated concepts, introductions, historical remarks, side notes concerning connections with other disciplines, and numerous exercises, complete with detailed solutions, The title provides the reader with the tools needed to arrive naturally at practical implementations of the concepts and techniques discussed, allowing for the design of algorithms to solve problems.
Table of Contents
 Cover
 Title Page
 Copyright
 Preface
 Chapter 1: Introduction
 Chapter 2: A Few Thoughts Before the Formalization

Chapter 3: Propositional Logic
 3.1. Syntax and semantics
 3.2. The method of semantic tableaux
 3.3. Formal systems
 3.4. A formal system for PL (PC)
 3.5. The method of Davis and Putnam
 3.6. Semantic trees in PL
 3.7. The resolution method in PL
 3.8. Problems, strategies, and statements
 3.9. Horn clauses
 3.10. Algebraic point of view of propositional logic
 Chapter 4: Firstorder Terms

Chapter 5: FirstOrder Logic (FOL) or Predicate Logic (PL1, PC1)
 5.1. Syntax
 5.2. Semantics
 5.3. Semantic tableaux in FOL
 5.4. Unification in the method of semantic tableaux
 5.5. Toward a semidecision procedure for FOL
 5.6. Semantic trees in FOL
 5.7. The resolution method in FOL
 5.8. A decidable class: the monadic class
 5.9. Limits: Gödel’s (first) incompleteness theorem
 Chapter 6: Foundations of Logic Programming
 Chapter 7: Artificial Intelligence
 Chapter 8: Inference

Chapter 9: Problem Specification in Logical Languages

9.1. Equality
 9.1.1. When is it used?
 9.1.2. Some questions about equality
 9.1.3. Why is equality needed?
 9.1.4. What is equality?
 9.1.5. How to reason with equality?
 9.1.6. Specification without equality
 9.1.7. Axiomatization of equality
 9.1.8. Adding the definition of = and using the resolution method
 9.1.9. By adding specialized rules to the method of semantic tableaux
 9.1.10. By adding specialized rules to resolution
 9.2. Constraints
 9.3. Second Order Logic (SOL): a few notions

9.1. Equality

Chapter 10: NonClassical Logics
 10.1. Manyvalued logics
 10.2. Inaccurate concepts: fuzzy logic
 10.3. Modal logics
 10.4. Some elements of temporal logic
 Chapter 11: Knowledge and Logic: Some Notions
 Chapter 12: Solutions to the Exercises
 Bibliography
 Index
Product Information
 Title: Logic for Computer Science and Artificial Intelligence
 Author(s):
 Release date: August 2011
 Publisher(s): Wiley
 ISBN: 9781118604267