Deduction in Top-Down Inductive Learning

Abstract

This chapter reviews a flexible strategy for combining analytic and empirical learning to acquire conceptual descriptions in real domains such as fault diagnosis or medical diagnosis, in which imperfect and intractable theories are, in general, available. The learning strategy is seen in the framework of a learning model (presented by the authors in F. Bergadano and A. Giordana, 1988) based on a top-down specialization process, which can now be guided by interleaving deductive and inductive steps. The chapter highlights two main novelties in comparison to earlier ideas. The first novelty consists in the adoption of both logical and dependency relations for describing the domain theories, in which it is possible to distinguish axioms that are sure from axioms which are just possible and other ones which can be just partially specified. This knowledge representation formalism can be effectively exploited to reflect the real domain knowledge possessed by a technician and offers a good background to decide where and how to activate deductive or inductive steps, and where to hypothesize theory incompleteness and inconsistencies. The second novelty consists in the repertory and in the flexibility of the reasoning schemes that can be used in the learning process.