Integrating explanatory and descriptive learning in ILP

Abstract

A learning framework that combines the two frameworks of explanatory and descriptive Inductive Logic Programming (ILP) is presented. The induced hypotheses in this framework are pairs of the form (T, IC) where T is a definite clausal theory and IC is a set of integrity constraints. The two components allow us to combine complementary information from the same data by applying both explanatory and descriptive learning methods. This non-trivial integration is achieved using a nonmonotonic entailment relation for the basic notion of coverage in the combined language of rules and constraints where the constraints can restrict the conclusions derivable by the rules. We present a semantics for the new framework and then discuss different cases where combining information from explanatory and descriptive ILP could be useful. We present some basic algorithmic frameworks for learning in the new framework, and report on some preliminary experiments with encouraging results.