Efficient consequence finding

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2001, Pages 359-365

  Simon, Laurent ^a   Del Val, Alvaro ^b  

^a UFR 919 Laboratoire d'Informatique Pour la Mécanique et les Sciences de l'Ingénieur   (France)

^b UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CONSEQUENCE FINDINGS; EXPERIMENTAL STUDIES; FAULT-TREES; KNOWLEDGE COMPILATION; PRIME IMPLICATE; TARGET LANGUAGE; ZERO-SUPPRESSED BINARY DECISION DIAGRAMS;

ARTIFICIAL INTELLIGENCE;

EID: 84880880822     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. R. E. Bryant. Symbolic Boolean manipulation with ordered binary-decision diagrams. ACM Computing Surveys, 24(3):293-318, 1992.
2. M. Cadoli and F. M. Donini. A survey on knowledge compilation. AI Communications, 10:137-150, 1997. (Pubitemid 127669673)
3. A. K. Chandra and G. Markowsky. On the number of prime implicants. Discrete Mathematics, 24:7-11, 1978.
4. Ph. Chatalic and L. Simon. Davis and Putnam 40 years later: a first experimentation. Technical report, LRI, Orsay, France, 2000.
5. Ph. Chatalic and L. Simon. Multi-resolution on compressed sets of clauses. In Proc. ICTAI (Tools with AI), pp. 449-454, 2000.
6. Ph. Chatalic and L. Simon. Zres: The old Davis-Putnam procedure meets ZBDD. In CADE'00, Int. Conf. Automated Deduction, pp. 2-10, 2000.
7. M. Davis and H. Putnam. A computing procedure for quantification theory. Journal of the ACM, 7(3):201-215, 1960.
8. J. de Kleer, A. K. Mackworth, and R. Reiter. Characterizing diagnosis and systems. Artificial Intelligence, 56:197-222, 1992.
9. J. de Kleer. An assumption-based TMS. Artificial Intelligence, 28(2):127-162, 1986.
10. J. de Kleer. An improved incremental algorithm for generating prime implicates. In AAAI'92, Proc. 10th Nat. Conf. on Artificial Intelligence, pp. 780-785, 1992.
11. R. Dechter and I. Rish. Directional resolution: The Davis-Putnam procedure, revisited. In KR'94, Proc. 4th Int. Conf. on Knowledge Representation and Reasoning, pp. 134-145. Morgan Kaufmann, 1994.
12. A. del Val. An analysis of approximate knowledge compilation. In IJCAI'95, Proc. 14th Int. Joint Conf. on Artificial Intelligence, pp. 830-836, 1995.
13. A. del Val. A new method for consequence finding and compilation in restricted languages. In AAAI'99, 16th Nat. Conf. on Artificial Intelligence, pp. 259-264, 1999.
14. A. del Val. The complexity of restricted consequence finding and abduction. In AAAI'2000, Proc. 17th Nat. Conf. on Artificial Intelligence, pp. 337-342, 2000.
15. A. del Val. Tractable classes for directional resolution. In AAAI'2000, Proc. 17th Nat. Conf. on Artificial Intelligence, pp. 343-348, 2000.
16. The Dimacs challenge benchmarks. Originally available at ftp://ftp.rutgers.dimacs.edu/challenges/sat.
17. Y. El Fattah and R. Dechter. Diagnosing tree-decomposable circuits. In IJCAI'95, Proc. 14th Int. Joint Conf. on Artificial Intelligence, pp. 1742-1748, 1995.
18. K. Inoue. Linear resolution for consequence-finding. Artificial Intelligence, 56:301-353, 1992.
19. A. Kean and G. Tsiknis. An incremental method for generating prime implicants/implicates. Journal of Symbolic Computation, 9:185-206, 1990.
20. A. Kean and G. Tsiknis. Clause management systems. Computational Intelligence, 9:11-40, 1993.
21. P. Marquis. Consequence-finding algorithms. In D. Gabbay and Ph. Smets, editors, Handbook of Defeasible Reasoning and Uncertainty Management Systems (vol. 5): Algorithms for Defeasible and Uncertain Reasoning, pp. 41-145. Kluwer Academic Publishers, 1999.
22. Ph. Mathieu. L'utilisation de la logique trivaluée dans les systèmes experts. PhD thesis, Université des Sciences et Technologies de Lille, France, 1991.
23. S. Minato. Zero-suppressed BDDs for set manipulation in combinatorial problems. In Proc. 30th ACM/IEEE Design Automation Conf., pp. 272-277, 1993.
24. H. Prendinger, M. Ishizuka, and T. Yamamoto. The Hyper system: Knowledge reformation for efficient first-order hypothetical reasoning. In PRICAI'00, 6th Pacific Rim Int. Conf. on Artificial Intelligence, 2000.
25. A. Rauzy. Boolean models for reliability analysis: a benchmark. Tech. report, LaBRI, Univ. Bordeaux I.
26. R. Reiter and J. de Kleer. Foundations of assumption-based truth maintenance systems. In AAAI'87, 6th Nat. Conf. on Artificial Intelligence, 1987.
27. R. Schrag and J. Crawford. Implicates and prime implicates in random 3-SAT. Artificial Intelligence, 81:199-221, 1996.
28. B. Selman and H. Kautz. Knowledge compilation and theory approximation. Journal of the ACM, 43(2):193-224, 1996. (Pubitemid 126628034)
29. B. Selman and H. J. Levesque. Support set selection for abductive and default reasoning. Artificial Intelligence, 82:259-272, 1996. (Pubitemid 126374155)
30. P. Tison. Generalized consensus theory and application to the minimization of boolean circuits. IEEE Transactions on Computers, EC-16:446-456, 1967.