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Lazy Propositionalisation for Relational Learning

Érick Alphonse, Céline Rouveirol

A number of Inductive Logic Programming (ILP) systems have addressed the problem of learning First Order Logic (FOL) discriminant definitions by first reformulating the FOL learning problem into an attribute-value one and then applying efficient learning techniques dedicated to this simpler formalism. The complexity of such propositionalisation methods is now in the size of the reformulated problem which is exponential when tackling highly indeterminate relational problems. We propose a method that selectively propositionalises the FOL training set by interleaving boolean reformulation and algebraic resolution. It avoids, as much as possible, the generation of reformulated examples which are not relevant to the discrimination task, and still ensures that explicit correct and complete definitions are learned. We present PROPAL, an AQ-like algorithm, exploiting this lazy propositionalisation method and then provide a first empirical evaluation on a standard benchmark dataset for ILP, the Mutagenesis problem.

Keywords: Inductive Logic Programming, Propositionalisation, Boolean Learning, Test incorporation

Citation: Érick Alphonse, Céline Rouveirol: Lazy Propositionalisation for Relational Learning. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.256-260.

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ECAI-2000 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Humboldt University on behalf of Gesellschaft für Informatik.