Multistrategy Theory Revision: Induction and Abduction in INTHELEX

Abstract

This paper presents an integration of induction and abduction in INTHELEX, a prototypical incremental learning system. The refinement operators perform theory revision in a search space whose structure is induced by a quasi-ordering, derived from Plotkin's θ-subsumption, compliant with the principle of Object Identity. A reduced complexity of the refinement is obtained, without a major loss in terms of expressiveness. These inductive operators have been proven ideal for this search space. Abduction supports the inductive operators in the completion of the incoming new observations. Experiments have been run on a standard dataset about family trees as well as in the domain of document classification to prove the effectiveness of such multistrategy incremental learning system with respect to a classical batch algorithm.