Abstract
We propose an approach for the integration of abduction and induction in Logic Programming. In particular, we show how it is possible to learn an abductive logic program starting from an abductive background knowledge and a set of examples. By integrating Inductive Logic Programming with Abductive Logic Programming we can learn in presence of incomplete knowledge. Incomplete knowledge is handled by designating some pieces of information as abducibles, that is, possible hypotheses which can be assumed, provided that they are consistent with the current knowledge base. We then specialize the framework for FOIL, an ILP system adopting extensional coverage. In particular, we propose an extension of the FOIL algorithm that is able to learn from incomplete data.
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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.
F. Bergadano and D. Gunetti. Inductive Logic Programming. MIT press, 1996.
A. Brogi, E. Lamma, P. Mancarella, and P. Mello. An Abductive Framework for Extended Logic Programming. In Proceedings 3rd Int. Workshop on Logic Programming and Non Monotonic Reasoning, 1995.
Y. Dimopoulos and A. Kakas. Abduction and learning. In Advances in Inductive Logic Programming. IOS Press, 1996.
K. Eshghi and R.A. Kowalski. Abduction compared with Negation by Failure. In Proceedings of ICLP89, 1989.
F. Esposito, E. Lamma, D. Malerba, P. Mello, M. Milano, F. Riguzzi, and G. Semeraro. Learning abductive logic programs. In M. Denecker, L. De Raedt, P. Flach, and A. Kakas, editors, Proceedings of the ECAI96 Workshop on Abductive and Inductive Reasoning. Catholic University of Leuven, 1996.
N. Inuzuka, M. Kamo, N. Ishii, H. Seki, and H. Itoh. Top-down induction of logic programs from incomplete samples. In S. Muggleton, editor, Proceedings of the 6th International Workshop on Inductive Logic Programming, pages 119–136. Stockholm University, Royal Institute of Technology, 1996.
A.C. Kakas, R.A. Kowalski, and F. Toni. Abductive logic programming. Journal of Logic and Computation, 2:719–770, 1993.
A.C. Kalcas 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 and F. Riguzzi. Learning with abduction. Technical Report TR-96-15, University of Cyprus, Computer Science Department, 1996.
R. Michalski, J.G. Carbonell, and T.M. Mitchell (eds). Machine Learning-An Artificial Intelligence Approach. Springer Verlag, 1984.
R. Michalski, J.G. Carbonell, and T.M. Mitchell (eds). Machine Learning-An Artificial Intelligence Approach Vol. II. Morgan Kaufmann, 1986.
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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Lamma, E., Mello, P., Milano, M., Riguzzil, F. (1997). Introducing abduction into (Extensional) inductive logic programming systems. In: Lenzerini, M. (eds) AI*IA 97: Advances in Artificial Intelligence. AI*IA 1997. Lecture Notes in Computer Science, vol 1321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63576-9_107
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DOI: https://doi.org/10.1007/3-540-63576-9_107
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