Logic-Based Artificial Intelligence / Edition 1

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This landmark volume represents the culmination of over 40 years of research in the use of logic as a basis for representing and manipulating problems in the field of artificial intelligence. The use of logic as a basis for commonsense reasoning was started by John McCarthy in 1959. The collection consists of both original research and surveys of almost every subject that uses logic in AI, contributed by leading scientists, and grew out of preliminary work presented at the Workshop on Logic-Based Artificial Intelligence held in Washington, DC, June 1999. All papers have been extensively refereed and revised.
The introductory article presents background on research that has transpired since 1959 and discusses the significance of each chapter in this context. The topics covered in the book are commonsense reasoning, knowledge representation, nonmonotonic reasoning, logic for causation and actions, planning and problem solving, cognitive robotics, logic for agents and actions, inductive reasoning, possibilistic logic, logic and beliefs, logic and language, computational logic, knowledge base system implementations, and applications of theorem proving and logic programming.
Logic-Based Artificial Intelligence is invaluable to graduate students and researchers in artificial intelligence, and advanced methods for database and knowledge base systems. Logic-Based Artificial Intelligence will also be of interest to those applying theorem proving methods to problems in program and hardware verification, to those who deal with large knowledge base systems, those developing cognitive robotics, and for those interested in the solution of McCarthy's 1959 "oldest planning problem in AI: getting from home to the airport".

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Table of Contents

Contributing Authors. Preface. Part I: Introduction to Logic-Based Artificial Intelligence. 1. Introduction to Logic-Based Artificial Intelligence; J. Minker. Part II: Commonsense Reasoning. 2. Concepts of Logical AI; J. McCarthy. Part III: Knowledge Representation. 3. Two Approaches to Efficient Open-World Reasoning; G. De Giacomo, H. Levesque. 4. Declarative Problem-Solving in DLV; T. Eder, et al. Part IV: Nonmonotonic Reasoning. 5. The Role of Default Logic in Knowledge Representation; J.P. Delgrande, Torsten Schaub. 6. Approximations, stable operators, well-founded fixpoints, applications in nonmonotonic reasoning; M. Denecker, et al. Part V: Logic for Causation, Actions. 7. Getting to the Airport: The Oldest Planning Problem in AI; V. Lifschitz, et al. Part VI: Planning, Problem Solving. 8. Encoding Domain Knowledge for Propositional Planning; H. Kautz, B. Selman. 9. Functional Strips; H. Geffner. Part VII: Logic, Planning, High Level Robotics. 10. Planning with Natural Actions in the Situation Calculus; F. Pirri, R. Reiter. 11. Reinventing Shakey; M. Shanahan. Part VIII: Logic for Agents, Actions. 12. Reasoning Agents in Dynamic Domains; C. Baral, M. Gelfond. 13. Dynamic Logic for Reasoning about Actions, Agents; J.-J.Ch. Meyer. Part IX: Inductive Reasoning. 14. Logic-Based Machine Learning; S. Muggleton, F. Marginean. Part X: Possibilistic Logic. 15. Decision, Nonmonotonic Reasoning, Possibilistic Logic; S. Benferhat, etal. Part Xl: Logic, Beliefs. 16. The Role(s) of Belief in AI; D. Perlis. 17. Modeling the Beliefs of Other Agents; R.H. Thomason. Part XII: Logic, Language. 18. The Situations We Talk about; L.K. Schuberi. Part XIII: Computational Logic. 19. Linear Time Datalog, Branching.Time Logic; G. Goitlob, et al. 20. On the Expressive Power of Planning Formalisms; B. Nebel. Part XIV: Knowledge Base System Implementations. 21. Extending the Smodels System with Cardinality, Weight Constraints; I. Niemelä, P. Simons. 22. Nonmonotonic Reasoning in LDL++; H. Wang, C. Zaniolo. Part XV: Applications of Theorem Proving, Logic Programming. 23. Towards a Mechanically Checked Theory of Computation; J. Strother Moore. 24. Logic-Based Techniques in Data Integration; A.Y. Levy. Index.

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