Abstract
In symbolic Machine Learning, the incremental setting allows to refine/revise the available model when new evidence proves it is inadequate, instead of learning a new model from scratch. In particular, specialization operators allow to revise the model when it covers a negative example. While specialization can be obtained by introducing negated preconditions in concept definitions, the state-of-the-art in Inductive Logic Programming provides only for specialization operators that can negate single literals. This simplification makes the operator unable to find a solution in some interesting real-world cases.
This paper proposes an empowered specialization operator for Datalog Horn clauses. It allows to negate conjunctions of pre-conditions using a representational trick based on predicate invention. The proposed implementation of the operator is used to study its behavior on toy problems purposely developed to stress it. Experimental results obtained embedding this operator in an existing learning system prove that the proposed approach is correct and viable even under quite complex conditions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ceri, S., Gottlöb, G., Tanca, L.: Logic Programming and Databases. Springer-Verlag, Heidelberg (1990)
Costa, V.S., Rocha, R., Damas, L.: The YAP Prolog system. Theory and Practice of Logic Programming 12(1–2), 5–34 (2012)
Esposito, F., Laterza, A., Malerba, D., Semeraro, G.: Locally finite, proper and complete operators for refining datalog programs. In: Michalewicz, M., Raś, Z.W. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 468–478. Springer, Heidelberg (1996)
Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy Theory Revision: Induction and Abduction in INTHELEX. Machine Learning Journal 38(1/2), 133–156 (2000)
Ferilli, S.: Toward an improved downward refinement operator for inductive logic programming. In: Atti del 11th Italian Convention on Computational Logic (CILC-2014), vol. 1195, pp. 99–113. Central Europe (CEUR) Workshop Proceedings (2014)
Kanellakis, P.C.: Elements of relational database theory. In: van Leeuwen, J. (ed.) Handbook of Theoretical Computer Science, Formal Models and Semantics, vol. B, pp. 1073–1156. Elsevier Science Publishers (1990)
Komorowski, J., Trcek, S.: Towards refinement of definite logic programs. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 315–325. Springer, Heidelberg (1994)
Laird, P.D.: Inductive inference by refinement. In: Proc. of AAAI-1986, Philadelphia, PA, pp. 472–476 (1986)
Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer-Verlag, Berlin (1987)
Nédellec, C., Rouveirol, C., Adé, H., Bergadano, F., Tausend, B.: Declarative bias in ILP. In: de Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 82–103. IOS Press, Amsterdam, NL (1996)
Reiter, R.: Equality and domain closure in first order databases. Journal of the ACM 27, 235–249 (1980)
Rouveirol, C.: Extensions of inversion of resolution applied to theory completion. In: Inductive Logic Programming, pp. 64–90. Academic Press (1992)
Semeraro, G., Esposito, F., Malerba, D.: Ideal refinement of datalog programs. In: Proietti, M. (ed.) LOPSTR 1995. LNCS, vol. 1048, pp. 120–136. Springer, Heidelberg (1996)
Semeraro, G., Esposito, F., Malerba, D., Fanizzi, N., Ferilli, S.: A logic framework for the incremental inductive synthesis of datalog theories. In: Fuchs, N.E. (ed.) LOPSTR 1997. LNCS, vol. 1463, pp. 300–321. Springer, Heidelberg (1998)
Shapiro, E.Y.: Inductive inference of theories from facts. Technical Report Research Report 192, Yale University (1981)
Siekmann, J.H.: An introduction to unification theory. In: Banerji, R.B. (ed.) Formal Techniques in Artificial Intelligence - A Sourcebook, pp. 460–464. Elsevier Science Publisher (1990)
Tinkham, N.L.: Schema induction for logic program synthesis. Artif. Intell. 98(1–2), 1–47 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ferilli, S., Pazienza, A., Esposito, F. (2015). Empowered Negative Specialization in Inductive Logic Programming. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds) AI*IA 2015 Advances in Artificial Intelligence. AI*IA 2015. Lecture Notes in Computer Science(), vol 9336. Springer, Cham. https://doi.org/10.1007/978-3-319-24309-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-24309-2_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24308-5
Online ISBN: 978-3-319-24309-2
eBook Packages: Computer ScienceComputer Science (R0)