FIRST-ORDER REGRESSION

We present a new approach, called First Order Regression (FOR), to han dling numerical information in Inductive Logic Programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic de scriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program FORS is an implementation of this idea, where numerical regression is focus ed on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP prob lem. Applications of FORS On several real-world data sets are describe d: the prediction of mutagenicity of chemicals, the modelling of liqui d dynamics in a surge tank, predicting the roughness in steel grinding , finite element mesh design, and operator's skill reconstruction in e lectric discharge machining. A comparison of FORS' performance with pr evious results in these domains indicates that FORS is an effective to ol for ILP applications that involve numerical data.