Abstract
A novel framework for combining logical constraints with theory induction in Inductive Logic Programming is presented. The constraints are solved using a boolean satisfiability solver (SAT solver) to obtain a candidate solution. This speeds up induction by avoiding generation of unnecessary candidates with respect to the constraints. Moreover, using a complete SAT solver, search space exhaustion is always detectable, leading to faster small clause/base case induction. We run benchmarks using two constraints: input-output specification and search space pruning. The benchmarks suggest our constraint satisfaction approach can speed up theory induction by four orders of magnitude or more, making certain intractable problems tractable.
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Notes
- 1.
Available at http://www.doc.ic.ac.uk/~shm/Software/progol4.4/
- 2.
Available with our source code distribution upon request.
- 3.
We could not measure the exact proportion for the tests that timed out, but it is estimated to be even less than its easier variants, thus always less than \(0.1\,\%\).
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Acknowledgments
The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 124409].
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Ahlgren, J., Yuen, S.Y. (2013). A Constraint Satisfaction Approach to Tractable Theory Induction. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_3
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DOI: https://doi.org/10.1007/978-3-642-44973-4_3
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