Generalization by absorption of definite clauses

Journal of Logic Programming

Volumn 40, Issue 2, 1999, Pages 127-157

  Taylor, Kerry ^a  

^a CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SOFTWARE; INVERSE PROBLEMS; KNOWLEDGE REPRESENTATION; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS;

BUNTINE MODEL; INDUCTIVE LOGIC PROGRAMMING; INVERSE RESOLUTION OPERATORS;

LOGIC PROGRAMMING;

EID: 0032634683     PISSN: 07431066     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0743-1066(99)00016-3     Document Type: Article

References (27)

1. R.B. Banerji, Learning theoretical terms, in: S. Muggleton (Ed.), Proceedings of the International Workshop on Inductive Logic Programming ILP-91 (1991).
2. W. Buntine, Generalized subsumption and its application to induction and redundancy, Artificial Intelligence 36 (1988) 149-176.
3. D. Hume, C. Sammut, Using inverse resolution to learn relations from experiments, in: L.A. Birnbaum, G.C. Collins (Eds.), Machine Learning: Proceedings of the Eighth International Workshop, Morgan Kaufmann, California, 1991.
4. P. Idestam-Almquist, Learning missing clauses by inverse resolution, in: Proceedings of the International Conference on Fifth Generation Computer Systems, Institute for New Generation Computer Technology, Ohmsha, 1992.
5. P. Idestam-Almquist, Generalization of clauses under implication, Journal of Artificial Intelligence Research 3 (1995) 467-489.
6. B. Jung, On inverting generality relations, in: S. Muggleton (Ed.), Proceedings of the International Workshop on Inductive Logic Programming ILP-93, Josef Stefan Institute, Technical Report, Slovenia, 1993.
7. S. Lapointe, S. Matwin, Sub-unification: A tool for efficient induction of recursive programs, in: D. Sleeman (Ed.), Machine Learning: Proceedings of the Ninth International Workshop, Morgan Kaufmann, CA, 1992.
8. X.C. Ling, M.A. Narayan, A critical comparison of various methods based on inverse resolution, in: L.A. Birnbaum, G.C. Collins (Eds.), Machine Learning: Proceedings of the Eighth International Workshop, Morgan Kaufmann, CA, 1991.
9. J.W. Lloyd, Foundations of Logic Programming, 2nd ed., Springer, Berlin, 1987.
10. R.S. Michalski, A theory and methodology of inductive learning, in: R.S. Michalski, J.G. Carbonell, T.M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Tioga, 1983.
11. S. Muggleton, Inductive logic programming, New Generation Computing 9 (4) (1991) 295-318.
12. S. Muggleton, W. Buntine, Machine invention of first-order predicates by inverting resolution, in: J. Laird (Ed.), Proceedings of the Fifth International Conference on Machine Learning, Morgan Kaufmann, CA, 1988.
13. S. Muggleton, C. Feng, Efficient induction of logic programs, in: Proceedings of the First Conference on Algorithmic Learning Theory, Tokyo, 1990.
14. S. Muggleton, D. Page, Self-saturation of definite clauses, in: S. Wrobel (Ed.), Proceedings of the International Workshop on Inductive Logic Programming ILP-94, Gesellschaft für Mathematik und Datenverarbeitung mbH, Technical Report 237, D-53754 Sankt Augustin, Germany, 1994.
15. S.H. Nienhuys-Cheng, P.A. Flach, Consistent term mappings, term partitions and inverse resolution, in: Y. Kodratoff (Ed.), Machine Learning: Proceedings EWSL-91, Springer, Berlin, 1991.
16. G.D. Plotkin, A note on inductive generalisation, in: B. Meltzer, D. Michie (Eds.), Machine Intelligence, vol. 5, Elsevier, North-Holland, 1970.
17. B.L. Richards, R.J. Mooney, Automated refinement of first-order horn-clause domain theories, Machine Learning 19 (2) (1995) 95-131.
18. J.A. Robinson, A machine oriented logic based on the resolution principle, Journal of the ACM 12 (1) (1965) 23-41.
19. C. Rouveirol, ITOU: Induction of first order theories, in: S. Muggleton (Ed.), Proceedings of the International Workshop on Inductive Logic Programming ILP-91, 1991.
20. C. Rouveirol, Semantic model for induction of first order theories, in: J. Mylopoulos, R. Reiter (Eds.), Proceedings of the Twelth International Joint Conference on Artificial Intelligence, vol. 2, Morgan Kaufmann, CA, 1991.
21. C. Rouveirol, Flattening and saturation: Two representation changes for generalisation, Machine Learning 14 (1994) 219-232.
22. C. Rouveirol, J.F. Puget, Beyond inversion of resolution, in: Proceedings of the Seventh International Conference on Machine Learning, Morgan Kaufmann, CA, 1990.
23. C. Rouveirol, J.-F. Puget, A simple solution for inverting resolution, in: K. Morik (Ed.), EWSL-89: Proceedings of the Fourth European Working Session on Learning, Pitman, London, 1990.
24. C. Sammut, Learning Concepts by Performing Experiments, Ph.D. thesis, University of NSW, Sydney, Australia, 1981.
25. C. Sammut, R.B. Banerji, Learning concepts by asking questions, in: R.S. Michalski, J.G. Carbonell (Eds.), Machine Learning: An Artificial Intelligence Approach, vol. 2, Morgan Kaufmann, CA, 1986.
26. K. Taylor, Inverse resolution of normal clauses, S. Muggleton (Ed.), Proceedings of the International Workshop on Inductive Logic Programming ILP-93, Josef Stefan Institute, Technical Report, Slovenia, 1993.
27. K. Taylor, Autonomous Learning by Incremental Induction and Revision, Ph.D. thesis, The Australian National University, Canberra, Australia, 1996.