Quantitative disjunctive logic programming: semantics and computation

AI Communications

Volumn 13, Issue 4, 2000, Pages 225-248

  Mateis, Cristinel ^a  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER PROGRAMMING LANGUAGES; KNOWLEDGE REPRESENTATION; MATHEMATICAL MODELS; SEMANTICS;

QUANTITATIVE DISJUNCTIVE LOGIC PROGRAMMING; UNCERTAINTY INFORMATION;

LOGIC PROGRAMMING;

EID: 0034505098     PISSN: 09217126     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (39)

1. F. Bacchus, Representing and reasoning with probabilistic knowledge, Research Report CS-88-31, University of Waterloo, 1988.
2. C. Baral and M. Gelfond, Logic programming and knowledge representation, Journal of Logic Programming 19/20(May/July) (1994), 73-148.
3. R. Ben-Eliyahu and R. Dechter, Prepositional semantics for disjunctive logic programs, Annals of Mathematics and Artificial Intelligence 12 (1994), 53-87.
4. R. Ben-Eliyahu and L. Palopoli, Reasoning with minimal models: efficient algorithms and applications, in: Proc. KR-94, 1994, pp. 39-50.
5. H.A. Blair and V.S. Subrahmanian, Paraconsistent logic programming, Theoretical Computer Science 68 (1987), 35-54.
6. P. Bonissone, Summarizing and propagating uncertain information with triangular norms, International Journal of Approximate Reasoning 1 (1987), 71-101.
7. M. Cadoli, T. Eiter and G. Gottlob, Default logic as a query language, IEEE Transactions on Knowledge and Data Engineering 9(3) (1997), 448-463.
8. P.R. Cohen and M.R. Grinberg, A framework for heuristic reasoning about uncertainty, in: Proc. IJCAI '83, Karlsruhe, Germany, 1983, pp. 355-357.
9. P.R. Cohen and M.R. Grinberg, A theory of heuristics reasoning about uncertainty, AI Magazine 4(2) (1983), 17-23.
10. A. Dekhtyar and V.S. Subrahmanian, Hybrid probabilistic programs, Journal of Logic Programming 43(3) (2000), 187-250.
11. A.P. Dempster, A generalization of Bayesian inference, J. of the Royal Statistical Society Series B 30 (1968), 205-247.
12. J. Dix, Semantics of logic programs: their intuitions and formal properties. An overview, in: Logic, Action and Information, DeGruyter, 1995, pp. 241-329.
13. J. Dix, M. Nanni and V.S. Subrahmanian, Probabilistic agent reasoning, in: Transactions of Computational Logic 1(2) (2000).
14. J. Doyle, Methodological simplicity in expert system construction: the case of judgements and reasoned assumptions, AI Magazine 4(2) (1983), 39-43.
15. T. Eiter, G. Gottlob and H. Mannila, Disjunctive datalog, ACM Transaction on Database System 22(3) (1997), 364-417.
16. T. Eiter and G. Gottlob, On the computational cost of disjunctive logic programming: propositional case, Annals of Mathematics and Artificial Intelligence 15(3/4) (1995), 289-323.
17. M.C. Fitting, Bilattices and the semantics of logic programming, J. Logic Programming 11 (1991), 91-116.
18. M. Gelfond and V. Lifschitz, Classical negation in logic programs and disjunctive databases, New Generation Computing 9 (1991), 365-385.
19. M. Ishizuka, Inference methods based on extended Dempster-Shafer theory for problems with uncertainty/fuzziness, New Generation Computing 1(2) (1983), 159-168.
20. M. Kifer and A. Li, On the semantics of rule-based expert systems with uncertainty, in: 2-nd International Conference on Database Theory, Springer-Verlag LNCS 326, 1988, pp. 102-117.
21. M. Kifer and V.S. Subrahmanian, Theory of the generalized annotated logic programming and its applications, J. Logic Programming 12 (1992), 335-367.
22. L.V.S. Lakshmanan, N. Leone, R. Ross and V.S. Subrahmanian, Prob View: a flexible probabilistic database system, ACM Transaction on Database Systems 22(3) (1997), 419-469.
23. J.W. Lloyd, Foundations of Logic Programming, Springer-Verlag, 1987.
24. J. Lobo, J. Minker and A. Rajasekar, Foundations of Disjunctive Logic Programming, MIT Press, Cambridge, MA, 1992.
25. C. Mateis, A quantitative extension of disjunctive logic programming, PhD Thesis, Technical University of Vienna, 1998.
26. C. Mateis, Extending disjunctive logic programming by T-norms, in: 5th International Conference, LPNMR '99, Springer-Verlag LNAI 1730, El Paso, Texas, USA, 1999, pp. 290-304.
27. R.T. Ng and V.S. Subrahmanian, Probabilistic logic programming, Information and Computation 101 (1992), 150-201.
28. R.T. Ng and V.S. Subrahmanian, Empirical probabilities in monadic deductive databases, in: Proc. Eighth Conf. Uncertainty in AI, Stanford, 1992, pp. 215-222.
29. R.T. Ng and V.S. Subrahmanian, A semantical framework for supporting subjective and conditional probabilities in deductive databases, J. of Automated Reasoning 10(2) (1993), 191-235.
30. R.T. Ng and V.S. Subrahmanian, Stable semantics for probabilistic deductive databases, Information and Computation 110 (1994), 42-83.
31. R.T. Ng and V.S. Subrahmanian, Non-monotonic negation in probabilistic deductive databases, in: Proc. 7-th Conf. Uncertainty in AI, Los Angeles, 1991, pp. 249-256.
32. N.J. Nilsson, Probabilistic logic, Artificial Intelligence 28 (1986), 71-87.
33. J. Pearl, Probabilistic Reasoning in Intelligent Systems - Networks of Plausible Inference, Morgan Kaufmann, 1988.
34. B. Schweizer and A. Sklar, Associative functions and abstract semi-groups, Publicationes Mathematicae Debrecen 10 (1963), 69-81.
35. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, 1976.
36. E. Shapiro, Logic programs with uncertainties: a tool for implementing expert systems, in: Proc. IJCAI '83, 1983, pp. 529-532.
37. M.H. van Emden, Quantitative deduction and its fixpoint theory, The Journal of Logic Programming 1 (1986), 37-53.
38. M.H. van Emden and R.A. Kowalski, The semantics of predicate logic as a programming language, JACM 23(4) (1976), 733-742.
39. L.A. Zadeh, Fuzzy sets, Inform. and Control 8 (1965), 338-353.