Relational Representations that Facilitate Learning
Chad Cumby, Dan Roth · 1999
Given a collection of objects in the world, along with some relations that hold among them, a fundamental problem is how to learn denitions of some relations and concepts of interest in terms of the given relations. These denitions might be quite complex and, inevitably, might require the use of quanti- ed expressions. Attempts to use rst order languages for these purposes are hampered by the fact that relational inference is intractable and, consequently, so is the problem of learning relational denitions. This work develops an expressive relational representation language that allows the use of propositional learning algorithms when learning relational denitions. The representation serves as an intermediate level between a raw description of observations in the world and a propositional learning system that attempts to learn denitions for concepts and relations. It allows for hierarchical composition of relational expressions that can be evaluated ecientl...