Induction of Recursive Theories in the Normal ILP Setting: Issues and Solutions

Abstract

Abstract. Induction of recursive theories in the normal ILP setting is a complex task because of the non-monotonicity of the consistency property. In this paper we propose computational solutions to some relevant issues raised by the multiple predicate learning problem. A separate-and-parallel-conquer search strategy is adopted to interleave the learning of clauses supplying predicates with mutually recursive definitions. A novel generality order to be imposed to the search space of clauses is investigated in order to cope with recursion in a more suitable way. The consistency recovery is performed by reformulating the current theory and by applying a layering technique based on the collapsed dependency graph. The proposed approach has been implemented in the ILP system ATRE and tested in the specific context of the document understanding problem within the WISDOM project. Experimental results are discussed and future directions are drawn.

Adaptive Layout Analysis of Document Images

“…It solves the following learning problem: The generalization model provides the basis for organizing the search space, since it establishes when a hypothesis explains a positive/negative example and when a hypothesis is more general/specific than another. The generalization model adopted by ATRE, called generalized implication, is explained in [7].…”

First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic Maps

“…ATRE implements a novel approach to the induction of recursive theories [9]. To illustrate how the main procedure works, let us consider the following instance of the learning problem: 7 ), onthesea(zone 7 …”