---
title: "FGP: A Virtual Machine for Acquiring Knowledge from Cases"
authors:
  - "Scott Fertig"
  - "David Gelernter"
doi: ""
url: "https://ijcai.org/proceedings/1991/117"
pdf: "https://ijcai.org/proceedings/1991/0117.pdf"
venue: "Proceedings of the Twelfth International Joint Conference on Artificial Intelligence"
year: 1991
pages: "796-801"
track: "Learning Be Knowledge Acquisition"
keywords:
---

# FGP: A Virtual Machine for Acquiring Knowledge from Cases

Scott Fertig, David Gelernter

*Proceedings of the Twelfth International Joint Conference on Artificial Intelligence*, pages 796-801.

## Abstract

Large Ca.':le databa.':les 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 databa.':le into rules, frames, or any other fixed abstraction . Implementations of the FGP machine use similarity-bMed reminding and the cMes themselves to drive the inference engine. By having the system calculate and incorporate a mea.':lure 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 clMsification tMk in three application area.':l.