Integrating Explanatory and Descriptive Learning in ILP

A learning framework that combines the two frameworks of explanatory and descriptive In ­ ductive Logic Programming (ILP) is presented. The induced hypotheses in this framework are pairs of the form (T, IC) where T is a defi­ nite 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-tr ivial integration is achieved using a nonmonotonic entailment relation for the basic notion of cov­ erage 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 combin­ ing information from explanatory and descrip­ tive ILP could be useful. We present some basic algorithmic frameworks for learning in the new framework, and report on some preliminary ex­ periments wi th encouraging results.