A logic for approximate first-order reasoning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2142, Issue , 2001, Pages 262-276

  Koriche, Frédéric ^a  

^a LIRMM   (France)

Author keywords

Approximate reasoning; First order logic; Multi modal logics; Resource bounded algorithms

Indexed keywords

APPROXIMATION ALGORITHMS; APPROXIMATION THEORY; FORMAL LOGIC; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION;

APPROXIMATE REASONING; FIRST ORDER LOGIC; LOGICAL CONSEQUENCES; MULTI-MODAL LOGIC; PROPOSITIONAL SATISFIABILITY; RESOURCE-BOUNDED; SIMPLE MODIFICATIONS; UNIVERSES OF DISCOURSE;

COMPUTER CIRCUITS;

EID: 84886788387     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44802-0_19     Document Type: Conference Paper

References (30)

1. N. D. Belnap. A useful four-valued logic. In Modern Uses of Multiple-Valued Logic, pages 8–37. Reidel, Dordrecht, 1977.
2. M. Cadoli and M. Schaerf. Approximate inference in default reasoning and circumscription. Fundamenta Informaticae, 2:123–143, 1995.
3. C. Chang and R. C. Lee. Symbolic Logic and Mechanical Theorem Proving. Academic Press Inc., 1973.
4. H. Chu and D. A. Plaisted. CLIN-S: A semantically guided first-order theorem prover. Journal of Automated Reasoning, 18(2):183–188, 1997.
5. M. Dalal. Anytime clausal reasoning. Annals of Mathematics and Artificial Intelligence, 22(3-4):297–318, 1998.
6. A. del Val. Tractable databases: How to make propositional unit resolution complete through compilation. In Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, pages 551–561, 1994.
7. J. W. Garson. Quantification in modal logic. In D. Gabbay and F. Guenthner, editors, Handbook of Philosophical Logic, Volume II: Extensions of Classical Logic, pages 249–307. D. Reidel Publishing Co., 1984.
8. J. Gu, P. W. Purdom, J. Franco, and B. W. Wah. Algorithms for satisfiability problem: A survey. In Satisfiability Problem: Theory and Applications, volume 35, pages 19–152. American Mathematical Society, 1997.
9. J. Hintikka. Knowledge and Belief. Cornell University Press, 1962.
10. D. W. Etherington J. M. Crawford. A non-deterministic semantics for tractable inference. In Proceedings of the 15th National Conference on Artificial Intelligence, pages 286–291, 1998.
11. H. Kautz, D. McAllester, and B. Selman. Encoding plans in propositional logic. In Principles of Knowledge Representation and Reasoning, pages 374–384, 1996.
12. H. Kautz and B. Selman. Pushing the envelope: Planning, propositional logic and stochastic search. In Proceedings of the 13th National Conference on Artificial Intelligence, pages 1194–1201, 1996.
13. S. Kim and H. Zhang. ModGen: Theorem proving by model generation. In Proc. of the 12th National Conference on Artificial Intelligence, pages 162–167, 1994.
14. F. Koriche. Approximate reasoning about combined knowledge. In Intelligent Agents Volume IV, volume 1365 of LNAI, pages 259–273. Springer Verlag, 1998.
15. F. Koriche. A logic for anytime deduction and anytime compilation. In Logics in Artificial Intelligence, volume 1489 of LNAI, pages 324–342. Springer Verlag, 1998.
16. G. Lakemeyer. Limited reasoning in first-order knowledge bases. Artificial Intelligence, 71:213–255, 1994.
17. G. Lakemeyer. Limited reasoning in first-order knowledge bases with full introspection. Artificial Intelligence, 84:209–255, 1996.
18. S. J. Lee and D. A. Plaisted. Problem solving by searching for models with a theorem prover. Artificial Intelligence, 69(1-2):205–233, 1994.
19. H. J. Levesque. Foundations of a Functional Approach to Knowledge Representation. Artificial Intelligence, 23:125–212, 1984.
20. H. J. Levesque. A logic of implicit and explicit belief. In Proceedings of the 6th National Conference on Artificial Intelligence, pages 198–202, 1984.
21. H. J. Levesque and R. J. Brachman. Expressiveness and tractability in knowledge representation and reasoning. Computational Intelligence, 3(2):78–93, 1987.
22. F. Massacci. Anytime approximate modal reasoning. In Proceedings of 15th National Conference on Artificial Intelligence, pages 274–279, 1998.
23. R. Reiter. What should a database know? In Proceedings of the 7th ACM Symposium on Principles of Database Systems, pages 302–304, 1988.
24. M. Schaerf and M. Cadoli. Tractable reasoning via approximation. Artificial Intelligence, 74:249–310, 1995.
25. J. Slaney. SCOTT: A model-guided theorem prover. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 109–115, 1993.
26. M. Y. Vardi. On the complexity of bounded-variable queries. In Proceedings of the 14th ACM Symposium on Principles of Database Systems, pages 266–276, 1995.
27. H. Zhang and M. E. Stickel. Implementing the davis-putnam method. Journal of Automated Reasoning, 24(1/2):277–296, 2000.
28. J. Zhang and H. Zhang. SEM: a system for enumerating models. In Proc. of the 14th International Joint Conference on Artificial Intelligence, pages 298–303, 1995.
29. Y. Zhu. Efficient First-Order Semantic Deduction Techniques. PhD thesis, University of North Carolina at Chapel Hill (USA), 1998.
30. S. Zilberstein. Using anytime algorithms in intelligent systems. AI Magazine, 17(3):73–83, 1996.