Revising first-order logic theories from examples through stochastic local search

Authors:
Aline Paes;Gerson Zaverucha;Vitor Santos Costa
Affiliations:
Department of Systems Engineering and Computer Science, COPPE, Federal University of Rio de Janeiro, Brazil;Department of Systems Engineering and Computer Science, COPPE, Federal University of Rio de Janeiro, Brazil;LIACC and DCC, FCUP, Universidade do Porto, Portugal
Venue:
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Year:
2007

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Abstract

First-Order Theory Revision from Examples is the process of improving user-defined or automatically generated First-Order Logic (FOL) theories, given a set of examples. So far, the usefulness of Theory Revision systems has been limited by the cost of searching the huge search spaces they generate. This is a general difficulty when learning FOL theories but recent work showed that Stochastic Local Search (SLS) techniques may be effective, at least when learning FOL theories from scratch. Motivated by these results, we propose novel SLS based search strategies for First-Order Theory Revision from Examples. Experimental results show that introducing stochastic search significantly speeds up the runtime performance and improve accuracy.