Inverse Narrowing for the Inductive Inference of Functional Logic Programs
José Hernández-Orallo
MJose Ramírez-Quintana
Abstract
We present a framework for the Induction of Functional Logic Programs
(IFLP) from facts. This can be seen as an extension to the now consolidated
field of Inductive Logic Programming (ILP). Inspired in the inverse resolution operator of ILP, we study
the reversal of narrowing, the more usual operational mechanism
for Functional Logic Programming. We also generalize the selection criteria for guiding the search, including coherence criteria in addition to the
MDL principle. A non-incremental learning algorithm and a
more sophisticated incremental extension of it are presented. We discuss the
advantages of IFLP over ILP, most of which are inherited from the power of
narrowing w.r.t. resolution.
At the end of this paper, we comment on the plausibility of extending the presented techniques to higher-order induction and its
appropriateness for function invention, a topic
which is difficult to incorporate homogeneously with the basic first-order
inductive rules of inference in ILP.
Key words: Functional Logic Programming, Inductive Logic Programming.
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