A Constraint Satisfaction Approach to Tractable Theory Induction

Conference paper

DOI: 10.1007/978-3-642-44973-4_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7997)
Cite this paper as:
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

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.

Keywords

Inductive Logic Programming Theory induction Constraint satisfaction 

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.City University of Hong KongHong KongChina

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