An extended transformation approach to Inductive Logic Programming

Nada Lavrac, Peter Flach, An extended transformation approach to Inductive Logic Programming. CSTR-00-002, Department of Computer Science, University of Bristol. March 2000. PDF, 166 Kbytes.


Inductive Logic Programming (ILP) is concerned with learning relational descriptions that typically have the form of logic programs. In a transformation approach, an ILP task is transformed into an equivalent learning task in a different representation formalism. Propositionalisation is a particular transformation method, in which the ILP task is compiled down to an attribute-value learning task. The main restriction of propositionalisation methods such a s LINUS is that they are unable to deal with non-determinate local variables in the body of hypothesis clauses. In this paper we show how this limitation can be overcome, by systematic first-order feature construction using a particular individual-centred feature bias. The approach can be applied in any domain where there is a clear notion of individual. We also show how to improve upon exhaustive first-order feature construction by using a relevancy filter. The proposed approach is illustrated on the ``trains'' and ``mutagenesis'' ILP domains.

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