Abstract
We present the system LAP (Learning Abductive Programs) that is able to learn abductive logic programs from examples and from a background abductive theory. A new type of induction problem has been defined as an extension of the Inductive Logic Programming framework. In the new problem definition, both the background and the target theories are abductive logic programs and abductive derivability is used as the coverage relation.
LAP is based on the basic top-down ILP algorithm that has been suitably extended. In particular, coverage of examples is tested by using the abductive proof procedure defined by Kakas and Mancarella [24]. Assumptions can be made in order to cover positive examples and to avoid the coverage of negative ones, and these assumptions can be used as new training data. LAP can be applied for learning in the presence of incomplete knowledge and for learning exceptions to classification rules.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
H. Adé and M. Denecker. RUTH: An ILP theory revision system. In Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems, 1994.
H. Adé and M. Denecker. AILP: Abductive inductive logic programming. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1995.
J. J. Alferes and L. M. Pereira. Reasoning with Logic Programming, volume 1111 of LNAI. SV, Heidelberg, 1996.
M. Bain and S. Muggleton. Non-monotonic learning. In S. Muggleton, editor, Inductive Logic Programming, chapter 7, pages 145–161. Academic Press, 1992.
M. Bain and S. Muggleton. Non-monotonic learning. In S. Muggleton, editor, Inductive Logic Programming, pages 145–161. Academic Press, 1992.
F. Bergadano and D. Gunetti. Learning Clauses by Tracing Derivations. In Proceedings 4th Int. Workshop on Inductive Logic Programming, 1994.
F. Bergadano and D. Gunetti. Inductive Logic Programming. MIT press, 1995.
F. Bergadano, D. Gunetti, M. Nicosia, and G. Ruffo. Learning logic programs with negation as failure. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 107–123. IOS Press, 1996.
A. Brogi, E. Lamma, P. Mancarella, and P. Mello. A unifying view for logic programming with non-monotonic reasoning. Theoretical Computer Science, 184:1–59, 1997.
L. De Raedt and M. Bruynooghe. On negation and three-valued logic in interactive concept learning. In Proceedings of the 9th European Conference on Artificial Intelligence, 1990.
L. De Raedt and M. Bruynooghe. Belief updating from integrity constraints and queries. Artificial Intelligence, 53:291–307, 1992.
L. De Raedt and M. Bruynooghe. A theory of clausal discovery. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1993.
L. De Raedt, N. Lavrač, and S. DŽeroski. Multiple predicate learning. In S. Muggleton, editor, Proceedings of the 3rd International Workshop on Inductive Logic Programming, pages 221–240. J. Stefan Institute, 1993.
L. De Raedt and W. Van Lear. Inductive constraint logic. In Proceedings of the 5th International Workshop on Algorithmic Learning Theory, 1995.
M. Denecker, L. De Raedt, P. Flach, and A. Kakas, editors. Proceedings of ECAI96 Workshop on Abductive and Inductive Reasoning. Catholic University of Leuven, 1996.
Y. Dimopoulos and A. Kakas. Learning Non-monotonic Logic Programs: Learning Exceptions. In Proceedings of the 8th European Conference on Machine Learning, 1995.
Y. Dimopoulos and A. Kakas. Abduction and inductive learning. In Advances in Inductive Logic Programming. IOS Press, 1996.
P.M. Dung. Negation as hypothesis: An abductive foundation for logic programming. In K. Furukawa, editor, Proceedings of the 8th International Conference on Logic Programming, pages 3–17. MIT Press, 1991.
S. DŽeroski. Handling noise in inductive logic programming. Master's thesis, Faculty of Electrical Engineering and Computer Science, University of Ljubljana, 1991.
K. Eshghi and R.A. Kowalski. Abduction compared with Negation by Failure. In Proceedings of the 6th International Conference on Logic Programming, 1989.
F. Esposito, E. Lamma, D. Malerba, P. Mello, M. Milano, F. Riguzzi, and G. Semeraro. Learning abductive logic programs. In Denecker et al. [15].
C. Hartshorne and P. Weiss, editors. Collected Papers of Charles Sanders Peirce, 1931–1958, volume 2. Harvards University Press, 1965.
A.C. Kakas, R.A. Kowalski, and F. Toni. Abductive logic programming. Journal of Logic and Computation, 2:719–770, 1993.
A.C. Kakas and P. Mancarella. On the relation between truth maintenance and abduction. In Proceedings of the 2nd Pacific Rim International Conference on Artificial Intelligence, 1990.
A.C. Kakas, P. Mancarella, and P.M. Dung. The acceptability semantics for logic programs. In Proceedings of the 11th International Conference on Logic Programming, 1994.
A.C. Kakas and F. Riguzzi. Learning with abduction. Technical Report TR-96-15, University of Cyprus, Computer Science Department, 1996.
A.C. Kakas and F. Riguzzi. Learning with abduction. In Proceedings of the 7th International Workshop on Inductive Logic Programming, 1997.
E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Integrating induction and abduction in logic programming. To appear on Information Sciences.
E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Integrating Induction and Abduction in Logic Programming. In P. P. Wang, editor, Proceedings of the Third Joint Conference on Information Sciences, volume 2, pages 203–206, 1997.
E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Introducing Abduction into (Extensional) Inductive Logic Programming Systems. In M. Lenzerini, editor, AI * IA97, Advances in Artificial Intelligence, Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence, number 1321 in LNAI. Springer-Verlag, 1997.
N. Lavrač and S. DŽeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.
L. Martin and C. Vrain. A three-valued framework for the induction of general logic programs. In Advances in Inductive Logic Programming. IOS Press, 1996.
R. Michalski, J.G. Carbonell, and T.M. Mitchell (eds). Machine Learning — An Artificial Intelligence Approach. Springer-Verlag, 1984.
S. Muggleton. Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming, 13(3–4):245–286, 1995.
L. M. Pereira, C. V. Damásio, and J. J. Alferes. Diagnosis and debugging as contradiction removal. In L. M. Pereira and A. Nerode, editors, Proceedings of the 2nd International Workshop on Logic Programming and Non-monotonic Reasoning, pages 316–330. MIT Press, 1993.
D.L. Poole. A logical framework for default reasoning. Artificial Intelligence, 32, 1988.
J. R. Quinlan and R.M. Cameron-Jones. Induction of Logic Programs: FOIL and Related Systems. New Generation Computing, 13:287–312, 1995.
E. Shapiro. Algorithmic Program Debugging. MIT Press, 1983.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lamma, E., Mello, P., Milano, M., Riguzzi, F. (1998). A system for abductive learning of logic programs. In: Dix, J., Pereira, L.M., Przymusinski, T.C. (eds) Logic Programming and Knowledge Representation. LPKR 1997. Lecture Notes in Computer Science, vol 1471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054792
Download citation
DOI: https://doi.org/10.1007/BFb0054792
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64958-8
Online ISBN: 978-3-540-49872-8
eBook Packages: Springer Book Archive