Abstract
Recent work has shown how a meta-level approach to inductive logic programming, which uses a semantic-preserving transformation of a learning task into an abductive reasoning problem, can address a large class of multi-predicate, nonmonotonic learning in a sound and complete manner. An Answer Set Programming (ASP) implementation, called ASPAL, has been proposed that uses ASP fixed point computation to solve a learning task, thus delegating the search to the ASP solver. Although this meta-level approach has been shown to be very general and flexible, the scalability of its ASP implementation is constrained by the grounding of the meta-theory. In this paper we build upon these results and propose a new meta-level learning approach that overcomes the scalability problem of ASPAL by breaking the learning process up into small manageable steps and using theory revision over the meta-level representation of the hypothesis space to improve the hypothesis computed at each step. We empirically evaluate the computational gain with respect to ASPAL using two different answer set solvers.
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Notes
- 1.
Note that empty goals are equivalent to \(\top \).
- 2.
The full details of the learning tasks can be found at https://dl.dropboxusercontent.com/u/15091371/ILP2013_examples.pdf.
References
Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)
Bratko, I.: Refining complete hypotheses in ILP. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 44–55. Springer, Heidelberg (1999)
Corapi, D.: Nonmonotonic inductive logic programming as abductive search. Ph.D. thesis, Imperial College London (2011)
Corapi, D., Russo, A., Lupu, E.: Inductive logic programming as abductive search. In: Hermenegildo, M., Schaub, T. (eds.) Technical Communications of the 26th International Conference on Logic Programming (2010)
Corapi, D., Russo, A., Lupu, E.: Inductive logic programming in answer set programming. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds.) ILP 2011. LNCS, vol. 7207, pp. 91–97. Springer, Heidelberg (2012)
Corapi, D., Russo, A., Vos, M.D., Padget, J.A., Satoh, K.: Normative design using inductive learning. TPLP 11(4–5), 783–799 (2011)
Dimopoulos, Y., Kakas, A.: Learning non-monotonic logic programs: learning exceptions. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 122–137. Springer, Heidelberg (1995)
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Schneider, M.: Potassco: the Potsdam answer set solving collection. AI Commun. 24(2), 105–124 (2011)
Katzouris, N., Artikis, A., Paliouras, G.: Incremental learning of event definitions with inductive logic programming. CoRR abs/1402.5988 (2014)
Kimber, T.: Learning definite and normal logic programs by induction on failure. Ph.D. thesis, Imperial College London (2012)
Kimber, T., Broda, K., Russo, A.: Induction on failure: learning connected horn theories. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 169–181. Springer, Heidelberg (2009)
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The DLV system for knowledge representation and reasoning. ACM Trans. Comput. Logic 7(3), 499–562 (2006)
Lloyd, J.: Foundations of logic programming. Springer, New York (1984)
Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. Logic Program. 19–20(20), 629–679 (1994)
Muggleton, S.H., Santos, J.C.A., Tamaddoni-Nezhad, A.: TopLog: ILP using a logic program declarative bias. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 687–692. Springer, Heidelberg (2008)
Muggleton, S.H., Lin, D.: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. In: IJCAI (2013)
Ray, O.: Nonmonotonic abductive inductive learning. J. Appl. Logic 7(3), 329–340 (2008)
Sakama, C.: Nonmonotonic inductive logic programming. In: Eiter, T., Faber, W., Truszczyński, M. (eds.) LPNMR 2001. LNCS (LNAI), vol. 2173, pp. 62–80. Springer, Heidelberg (2001)
Sakama, C.: Induction from answer sets in nonmonotonic logic programs. ACM Trans. Comput. Logic 6(2), 203–231 (2005)
Sakama, C., Inoue, K.: Brave induction: a logical framework for learning from incomplete information. Mach. Learn. 67(1), 3–35 (2009)
Stahl, I.: Predicate invention in inductive logic programming. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 34–47. IOS Press, Amsterdam (1996)
Wrobel, S.: First order theory refinement. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 14–33. IOS Press, Amsterdam (1996)
Acknowledgment
This work is partially funded by the 7th Framework EU-FET project 600792 ALLOW Ensembles and the EPSRC project P44745.
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Athakravi, D., Corapi, D., Broda, K., Russo, A. (2014). Learning Through Hypothesis Refinement Using Answer Set Programming. In: Zaverucha, G., Santos Costa, V., Paes, A. (eds) Inductive Logic Programming. ILP 2013. Lecture Notes in Computer Science(), vol 8812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44923-3_3
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