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.
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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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