Concept Learning in a Rich Input Domain: Generalization-Based Memory

Abstract

Automatic concept learning from large amounts of complex input data is an interesting and difficult process. In this paper we discuss how the use of a permanent, generalization-based, memory can serve as an important tool in developing programs that learn in rich input domains. The use of Generalization-Based Memory (GBM) allows programs to determine what concepts to learn, as well as definitions of the concepts. We present in this paper a characterization of our research, describe our use of Generalization-Based Memory in two programs under development at Columbia, UNIMEM and RESEARCHER, and describe how they perform concept evaluation and generalization of complex structural descriptions, problems typical of those we are concerned with