Abstract
The bounded ILP-consistency problem for function-free Horn clauses is described as follows. Given a set E + and E − of function-free ground Horn clauses and an integer k polynomial in E +∪E −, does there exist a function-free Horn clause C with no more than k literais such that C subsumes each element in E + and C does not subsume any element in E −. It is shown that this problem is Σ P2 complete. We derive some related results on the complexity of ILP and discuss the usefulness of such complexity results.
This work has been supported by FWF (Austrian Science Funds) under the project P11580-MAT “A Query System for Disjunctive Deductive Databases” and by the ISICNR, Istituto per la Sistemistica e l'Inforrnatica (Italian National Research Council), under grant n.224.07.5.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
L.M. Adleman. Molecular Computation of Solutions to Combinatorial Problems. Science 266, pp. 1021–1024, 1994.
L. D. Baxter. The NP-completeness of Subsumption. Unpublished manuscript, 1977.
P. Cholewinski, V.W. Marek, and M. Truszczynski Default Reasoning System DeReS Proc. Fifth Intl. Conference on Principles of Knowledge Representation and Reasoning (KR'96), Cambridge, MA, Nov.5–8, 1996.
W. Cohen. PAC-Learning Recursive Logic Programs: Efficient Algorithms. Journal of Artificial Intelligence Research, 2: 501–539, 1995.
W. Cohen. PAC-Learning Recursive Logic Programs: Negative Results. Journal of Artificial Intelligence Research, 2: 541–573, 1995.
W. Cohen and C. D. Page. Polynomial Learnability and Inductive Logic Programming — Methods and Results. New Generation Computing, 13(3-4): 369–409, 1995.
L. De Raedt and S. Džeroski. First order jk-clausal theories are PAC-learnable. Artificial Intelligence, 70: 375–392, 1994.
J. Dix and M. Müller. Implementing Semantics of Disjunctive Logic programs Using Fringes and Abstract properties. Proc. Second Intl. workshop on Logic Programming and Nonmonotonic reasoning (LPNMR-93), Lisbon, Portugal, July 1993, pp. 43–59, MIT Press.
T. Eiter and G. Gottlob. On the Computational Cost of Disjunctive Logic Programming: Propositional Case. Annals of Mathematics and Artificial Intelligence, 15(3/4):289–323, 1995.
T. Eiter, N. Leone, C. Mateis, G. Pfeifer, and F. Scarcello. A Deductive System for Nonmonotonic Reasoning. In Proc. 4th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR '97), Lecture Notes in AI (LNAI), J. Dix, U. Furbach, and A. Nerode Eds., Springer, Berlin, 1997 (to appear).
T. Eiter, G. Gottlob, and H. Mannila.Disjunctive Datalog. ACM Trans. on Database Syst., September 1997. To appear.
M. R. Garey and D. S. Johnson. Computers and Intractability — A guide to the Theory of NP-completeness. Freman, San Francisco, CA, 1979.
M. Gelfond and V. Lifschitz. The Stable Model Semantics for Logic Programming. In Logic Programming: Proc. Fifth Intl Conference and Symposium, pp. 1070–1080, Cambridge, Mass., 1988. MIT Press.
E. M. Gold. Language Identification in the Limit. Information and Control, 10:447–474, 1967.
G. Gottlob. Subsumption and Implication. Information Processing Letters, 24:109–111, 1987.
G. Gottlob. Complexity Results for Nonmonotonic Logics. J. Logic and Computation, 2(3):397–425, June 1992.
D. S. Johnson. A Catalog of Complexity Classes. In J. van Leeuwen, editor, Handbook of Theoretical Computer Science, volume A, chapter 2. Elsevier Science Publishers B.V. (North-Holland), 1990.
J.U. Kietz and S. Džeroski. Inductive logic programming and learnability. SIGART Bulletin 5(1): 22–32 (Special issue on Inductive Logic Programming), 1994.
D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant's model. Artificial Intelligence, 36(2):177–221, 1988.
R.J. Lipton. Using DNA to solve NP-complete Problems. Priceton University.
W. Marek and M. Truszczyński. Autoepistemic Logic. JACM, 38(3):588–619, 1991.
S. H. Muggleton. Inductive Logic Programming. New Generation Computing, 8(4):295–318, 1991.
Inductive Logic Programming, S. H. Muggleton ed., Academic Press, London, 1992.
S. H. Muggleton and L. De Raedt. Inductive Logic Programming: Theory and Methods. Journal of Logic Programming, 19,20:629–679, 1994.
Muggleton, S., and Page, D., A learnability model for universal representations. In Proc. Fourth International Workshop on Inductive Logic Programming, pages 139–160. GMD, Bonn, 1994.
C. D. Page and A. M. Frish. Generalization and Learnability: a study of constrained atoms. In Inductive Logic Programming, pp.29–61, S. H. Muggleton ed., Academic Press, London, 1992.
G. D. Plotkin. A note on Inductive Generalization. In Machine Intelligence, pp. 153–163, B. Meltzer and D. Michie eds., American Elsevier, 1970.
R. Reiter. A Logic for Default Reasoning. Artificial Intelligence, 13:81–132, 1980.
J. Robinson. A machine-oriented logic based on the resolution principle. Journal of the ACM, 12(1):23–41, 1965.
R. E. Schapire. The Strength of Weak Learnability. Machine Learning, 5:197–227, 1990.
Diana Rooiß and Klaus Wagner. On the Power of DNA Computation. Information and Computation, 131(2):95–109, 1996.
L. G. Valiant. A Theory of the Learnable. Communications of the ACM, 27:1134–1142.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gottlob, G., Leone, N., Scarcello, F. (1997). On the complexity of some Inductive Logic Programming problems. In: Lavrač, N., Džeroski, S. (eds) Inductive Logic Programming. ILP 1997. Lecture Notes in Computer Science, vol 1297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540635149_31
Download citation
DOI: https://doi.org/10.1007/3540635149_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63514-7
Online ISBN: 978-3-540-69587-5
eBook Packages: Springer Book Archive