Reducing inductive definitions to prepositional satisfiability

Lecture Notes in Computer Science

Volumn 3668, Issue , 2005, Pages 221-234

  Pelov, Nikolay ^a   Ternovska, Eugenia ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL MATHEMATICS; CONSTRAINT THEORY; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION; PROBLEM SOLVING; SEARCH ENGINES; SEMANTICS; SET THEORY;

ANSWER SET PROGRAMMING (ASP); MATHEMATICAL INDUCTION; MODEL SEMANTICS; SEARCH PROBLEMS;

LOGIC PROGRAMMING;

EID: 27144550410     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11562931_18     Document Type: Conference Paper

References (24)

1. G. Baral. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, 2003.
2. R. Ben-Eliyahu and R. Dechter. Prepositional semantics for disjunctive logic programs. Annals of Mathematics and Artificial Intelligence, 12(1-2):53-87, 1994.
3. R. J. Brachman and H. Levesque. Competence in knowledge representation. In Proc. of the National Conference on Artificial Intelligence, pages 189-192, 1982.
4. M. Denecker. Extending classical logic with inductive definitions. In Computational Logic, First International Conference, volume 1861 of Lecture Notes in Computer Science, pages 703-717. Springer, 2000.
5. M. Denecker and E. Ternovska. Inductive situation calculus. In Principles of Knowledge Representation and Reasoning: Proc. of the 9th International Conference, pages 545-553. AAAI Press, 2004.
6. M. Denecker and E. Ternovska. A logic of non-monotone inductive definitions and its modularity properties. In Logic Programming and Nonmonotonic Reasoning: 7th International Conference, volume 2923 of Lecture Notes in Computer Science, pages 47-60. Springer, 2004.
7. M. Denecker, D. Theseider Dupré, and K. Van Belleghem. An inductive definition approach to ramifications. Linköping Electronic Articles in Computer and Information Science, 3(7): 1-43, 1998. http://www.ep.liu.se/ea/cis/1998/007/.
8. E. Erdem and V. Lifschitz. Tight logic programs. Theory and Practice of Logic Programming, 3(4-5):499-518, 2003.
9. F. Fages. Consistency of Clark's completion and existence of stable models. Journal of Methods of Logic in Computer Science, 1:51-60, 1994.
10. M. Gelfond and V. Lifschitz. The stable model semantics for logic programming. In Logic Programming, 5th International Conference and Symposium, pages 1070-1080, 1988.
11. T. Janhunen. Representing normal programs with clauses. In Proc. of the 16th European Conference on Artificial Intelligence, pages 358-362, 2004.
12. Y. Lierler and M. Maratea. Cmodels-2: SAT-based answer set solver enhanced to non-tight programs. In Logic Programming and Nonmonotonic Reasoning, 7th International Conference, volume 2923 of Lecture Notes in Computer Science, pages 346-350. Springer, 2004.
13. F. Lin and J. Zhao. On tight logic programs and yet another translation from normal logic programs to propositional logic. In International Joint Conference on Artificial Intelligence, pages 853-858. Morgan Kaufmann, 2003.
14. F. Lin and Y. Zhao. ASSAT: Computing answer sets of a logic program by SAT solvers. Artificial Intelligence, 157(1-2):115-137, 2004.
15. T. Linke, H. Tompits, and S. Woltran. On acyclic and head-cycle free nested logic programs. In 20th International Conference on Logic Programming, volume 3132 of Lecture Notes in Computer Science, pages 225-239. Springer, 2004.
16. M. Mariën, D. Gilis, and M. Denecker. On the relation between ID-Logic and Answer Set Programming. In Logics in Artificial Intelligence, 9th European Conference (JELIA), volume 3229 of Lecture Notes in Computer Science, pages 108-120. Springer, 2004.
17. D. Mitchell and E. Ternovska. A framework for representing and solving NP-search problems. In Proc. of the National Conference on Artificial Intelligence, pages 430-435, 2005.
18. I. Niemelä. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3,4):241-273, 1999.
19. L. Ryan. Efficient algorithms for clause-learning SAT solvers. Master's thesis, Simon Fraser University, Burnaby, Canada, 2004.
20. P. Simons, I. Niemelä, and T. Soininen. Extending and implementing the stable model semantics. Artificial Intelligence, 138(1-2):181-234, 2002.
21. E. Ternovskaia. Causality via inductive definitions. In Working Notes of "Prospects for a Commonsense Theory of Causation", AAAI Spring Symposium Series, pages 94-100, 1998.
22. E. Ternovskaia. ID-logic and the ramification problem for the situation calculus. In Proc. of the 14th European Conference on Artificial Intelligence, pages 563-567, 2000.
23. A. Van Gelder, K. A. Ross, and J. S. Schlipf. The well-founded semantics for general logic programs. Journal of the ACM, 38(3):620-650, 1991.
24. B. Van Nuffelen, A. Cortés-Calabuig, M. Denecker, O. Arieli, and M. Bruynooghe. Data integration ising ID-logic. In Advanced Information Systems Engineering, Proc. of the 16th International Conference. (CAiSE), volume 3084 of Lecture Notes in Computer Science, pages 67-81. Springer, 2004.