Abstract
Traditional Machine Learning approaches are based on single inference mechanisms. A step forward concerned the integration of multiple inference strategies within a first-order logic learning framework, taking advantage of the benefits that each approach can bring. Specifically, abduction is exploited to complete the incoming information in order to handle cases of missing knowledge, and abstraction is exploited to eliminate superfluous details that can affect the performance of a learning system. However, these methods require some background information to exploit the specific inference strategy, that must be provided by a domain expert.
This work proposes algorithms to automatically discover such an information in order to make the learning task completely autonomous. The proposed methods have been tested on the system INTHELEX, and their effectiveness has been proven by experiments in a real-world domain.
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References
Cohen, P.R., Feigenbaum, E.A.: The Handbook of Artificial Intelligence, vol. 3. Morgan Kaufmann, San Francisco (1981)
De Raedt, L.: Interactive Theory Revision - An Inductive Logic Programming Approach. Academic Press, London (1992)
Dimopoulos, Y., Džeroski, S., Kakas, A.: Integrating explanatory and descriptive learning in ILP. In: Proceedings of IJCAI 1997, pp. 900–906 (1997)
Dimopoulos, Y., Kakas, A.: Abduction and learning. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 144–171. IOS Press, Amsterdam (1996)
Esposito, F., Ferilli, S., Fanizzi, N., Basile, T.M.A., Di Mauro, N.: Incremental multistrategy learning for document processing. Applied Artificial Intelligence: An Internationa Journal 17(8/9), 859–883 (2003)
Esposito, F., Lamma, E., Malerba, D., Mello, P., Milano, M., Riguzzi, F., Semeraro, G.: Learning abductive logic programs. In: Proceedings of the ECAI 1996 Workshop on Abductive and Inductive Reasoning (1996)
Ferilli, S., Esposito, F., Basile, T.M.A., Di Mauro, N.: Automatic induction of first-order logic descriptors type domains from observations. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, pp. 116–131. Springer, Heidelberg (2004)
Flach, P.A., Lachiche, N.: Confirmation-guided discovery of first-order rules with Tertius. Machine Learning 42(1/2), 61–95 (2001)
Giordana, A., Roverso, D., Saitta, L.: Abstracting concepts with inverse resolution. In: Proceedings of the 8th International Workshop on Machine Learning, Evanston, IL, pp. 142–146. Morgan Kaufmann, San Francisco (1991)
Kakas, A.C., Kowalski, R., Toni, F.: Abductive logic programming. Journal of Logic and Computation 2(6), 718–770 (1993)
Kakas, A.C., Mancarella, P.: Generalized stable models: a semantics for abduction. In: Proceedings of ECAI 1990, pp. 385–391. Pitman Publishing (1990)
Kakas, A.C., Mancarella, P.: On the relation of truth maintenance and abduction. In: Proceedings of the 1st Pacific Rim International Conference on Artificial Intelligence, Nagoya, Japan (1990)
Kietz, J.-U., Wrobel, S.: Controlling the complexity of learning in logic through syntactic and task-oriented models. In: Muggleton, S. (ed.) ILP 1991, pp. 107–126 (1991)
Lamma, E., Mello, P., Milano, M., Riguzzi, F., Esposito, F., Ferilli, S., Semeraro, G.: Cooperation of abduction and induction in logic programming. In: Kakas, A., Flach, P. (eds.) Abductive and Inductive Reasoning: Essays on their Relation and Integration. Kluwer, Dordrecht (2000)
Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Berlin (1987)
Michalski, R.S.: Inferential theory of learning. developing foundations for multistrategy learning. In: Michalski, R.S., Tecuci, G. (eds.) Machine Learning. A Multistrategy Approach, vol. IV, pp. 3–61. Morgan Kaufmann, San Francisco (1994)
Muggleton, S.H., De Raedt, L.: Inductive logic programming. Journal of Logic Programming: Theory and Methods 19, 629–679 (1994)
De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26(2), 99–146 (1997)
Rouveirol, C., Puget, J.: Beyond inversion of resolution. In: Proceedings of ICML 1997, Austin, TX, pp. 122–130. Morgan Kaufmann, San Francisco (1990)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Srinivasan, A.: The aleph manual version 4 (2003), http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/
Utgoff, P.E.: Shift of bias for inductive concept learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: an artificial intelligence approach, vol. II, pp. 107–148. Morgan Kaufmann, Los Altos (1986)
Zucker, J.-D.: Semantic abstraction for concept representation and learning. In: Michalski, R.S., Saitta, L. (eds.) Proceedings of the 4th International Workshop on Multistrategy Learning, pp. 157–164 (1998)
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Ferilli, S., Basile, T.M.A., Di Mauro, N., Esposito, F. (2005). Automatic Induction of Abduction and Abstraction Theories from Observations. In: Kramer, S., Pfahringer, B. (eds) Inductive Logic Programming. ILP 2005. Lecture Notes in Computer Science(), vol 3625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536314_7
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DOI: https://doi.org/10.1007/11536314_7
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