ILP Through Propositionalization and Stochastic k-Term DNF Learning

Authors:
Aline Paes;Filip Železný;Gerson Zaverucha;David Page;Ashwin Srinivasan
Affiliations:
Dept. of Systems Engineering and Computer Science - COPPE, Federal University of Rio de Janeiro (UFRJ),;Dept. of Cybernetics - School of Electrical Engineering, Czech Institute of Technology in Prague,;Dept. of Systems Engineering and Computer Science - COPPE, Federal University of Rio de Janeiro (UFRJ),;Dept. of Biostatistics and Medical Informatics and, Dept. of Computer Sciences, University of Wisconsin,;IBM India Research Laboratory and, Dept of CSE and Centre for Health Informatics, University of New South Wales, Sydney, Australia
Venue:
Inductive Logic Programming
Year:
2007

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Abstract

One promising family of search strategies to alleviate runtime and storage requirements of ILP systems is that of stochastic local search methods, which have been successfully applied to hard propositional tasks such as satisfiability. Stochastic local search algorithms for propositional satisfiability benefit from the ability to quickly test whether a truth assignment satisfies a formula. Because of that many possible solutions can be tested and scored in a short time. In contrast, testing whether a clause covers an example in ILP takes much longer, so that far fewer possible solutions can be tested in the same time. Therefore in this paper we investigate stochastic local search in ILP using a relational propositionalized problem instead of directly use the first-order clauses space of solutions.