θ-subsumption for structural matching

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

Volumn 1224, Issue , 1997, Pages 73-84

  De Raedt, Luc ^a   Idestam Almquist, Peter ^b   Sablon, Gunther ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS;

BACK-GROUND KNOWLEDGE; BACKGROUND THEORY; GENERALISATION; LITERALS; NONUNIQUENESS; STRUCTURAL MATCHING;

LEARNING SYSTEMS;

EID: 84947940031     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (26)

1. G. Bisson. Learning in FOL with a Similarity Measure. In Proceeding, of AAAI, 1992.
2. L. De Raedt. Interactive Theory Revision: an Inductive Logic Programming approach, Academic Press, 1992.
3. L. De Raedt and M. Bruynooghe. A theory of clausal discovery. In Proceedings of the 13th International Joint Conference on Artificial lntelligence, pages 1058-1063. Morgan Kaufmann, 1993.
4. L. De Raedt and S. Džeroski. First order jk-clausal theories are PAC-learnable. Artificial Intelligence, 70:375-392, 1994.
5. L. De Raedt. Induction in Logic. In Proceedings of the 3rd Multistrategy Learning Workshop, 1996.
6. J. G. Ganascia and Y. Kodratoff. Improving the generalization step in learning. In R. S Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: an artificial intelligence approach, volume 2, pages 215-241. Morgan Kaufmann, 1986.
7. R. Gemello, F. Mann, and L. Saitta. Rigel: An inductive learning system. Machine Learning, 6:7-35, 1991.
8. F. Hayes-Roth and J. McDermott. An interference matching technique for inducing abstractions. Communications of the ACM, 21:401-410, 1978.
9. P. Idestam-Almquist. Generalisation under implication using or-introduction. In Proceedings of the 6th European Conference on Machine Learning, volume 667, pages 56-64. Lecture Notes in Artificial Intelligence, 1993.
10. P. Idestam-Almquist. Generalization of clauses relative to a theory Machine Learning, in press, 1997.
11. S. Lapointe and S. Matwin. Sub-unification: a tool for efficient induction of recursive programs. In Proceedings of the 9th International Workshop on Machine Learning. Morgan Kaufmann, 1992.
12. M. J. Maher. Equivalences of logic programs. In Proceedings of Third International Conference on Logic Programming, Berlin, 1986. Springer.
13. S. Muggleton and L. De Raedt. Inductive logic programming: theory and methods. Journal of Logic Programming, Vol. 19-20, 1994.
14. S. Muggleton. Inverting implication. Artificial Intelligence, 1994. To appear.
15. S. Muggleton. Inductive Logic Programming. New Generation Computing,8:295-317, 1991.
16. S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the 1st conference on algorithmic learning theory, pages 368-381. Ohmsma, Tokyo, Japan, 1990.
17. S. Muggleton and W. Buntine. Machine invention of first order predicates by inverting resolution. In Proceedings of the 5th International Workshop on Machine Learning, Morgan Kaufmann, 1988.
18. G. Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5, pages 153-163. Edinburgh University Press, 1970.
19. G. Plotkin. Automatic Methods of Inductive Inference. PhD thesis, Edinburgh University, 1971.
20. G. Plotkin. A further note on inductive generalization. In Machine Intelligence, volume 6, pages 101-124. Edinburgh University Press, 1971.
21. J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239-266, 1990.
22. G. Sablon, L. De Raedt, and M. Bruynooghe. Iterative Versionspaces. Artificial Intelligence, 69:393-409, 1994.
23. S. A. Vere. Induction of concepts in the predicate calculus. In Proceedings of the 4th International Joint Conference on Artificial Intelligence, pages 282-287. Morgan Kaufmann, 1975.
24. S. A. Vere. Induction of relational productions in the presence of background information. In Proceedings of the 5th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 1977.
25. S. A. Vere. Multilevel counterfactuals for generalizations of relational concepts and productions. Artificial Intelligence, 14:139-164,1980.
26. C. Vrain. Ogust: A system that learns using domain properties expressed as theorems. In Y. Kodratof and R. S. Michalski, editors, Machine Learning: an artificial intelligence approach, volume 3, pages 360-381. Morgan Kaufmann, 1990.