A Generalization Model Based on OI-implication for Ideal heory Refinement

Issue title: Intelligent Systems

Article type: Research Article

Authors: Esposito, Floriana | Fanizzi, Nicola | Ferilli, Stefano | Semeraro, Giovanni

Affiliations: Dipartimento di Informatica, Università degli Studi di Bari, Via E. Orabona, 4 I-70125 Bari, Italy

Abstract: A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning regarded as a search process. A refinement operator satisfying these requirements is formally defined as ideal. Past results have demonstrated the impossibility of specifying ideal operators in search spaces where standard generalization models, like logical implication or �-subsumption, are adopted. By assuming the object identity bias over a space defined by a clausal language ordered by logical implication, a novel generalization model, named OI-implication, is derived and we prove that ideal operators can be defined for the resulting search space.

Keywords: Incremental Learning, Theory Refinement, Refinement Operators, Generalization Models, �-subsumption, Implication

Journal: Fundamenta Informaticae, vol. 47, no. 1-2, pp. 15-33, 2001