FGP: A Virtual Machine for Acquiring Knowledge from Cases

Abstract

Large case databases are numerous and packed with information. The largest of them are potentially rich sources of domain knowledge. The FGP machine is a software architecture that can make this knowledge explicit and bring it to bear on classification and prediction problems. The architecture provides much of the functionality of traditional expert systems without requiring the system builder to preprocess the database into rules, frames, or any other fixed abstraction. Implementations of the FGP machine use similarity-based reminding and the cases themselves to drive the inference engine. By having the system calculate and incorporate a measure of feature salience into its distance calculations, the FGP architecture smoothly copes with incomplete data and is particularly well-suited to weak-theory domains. We explain the model, describe a particular implementation of it, and present test-results for a classification task in three application areas.