Heuristics for Empirical Discovery.

Abstract

In this paper, the authors review their experiences with the BACON project, which has focused on empirical methods for discovering numeric laws. The six successive versions of BACON have employed a variety of discovery methods, some very simple and others quite sophisticated. They examine methods for discovering a functional relation between two numeric terms, including techniques for detecting monotonic trends, finding constant differences, and hill-climbing through a space of parameter values. They also consider methods for discovering complex laws involving many terms, some of which build on techniques for finding two-variable relations. Finally, they introduce the notions of intrinsic properties and common divisors, and examine methods for inferring intrinsic values from symbolic data. In each case, they describe the various techniques in terms of the search required to discover useful laws.

Document Details

Document Type
Technical Report
Publication Date
Jun 25, 1984
Accession Number
ADA145324

Entities

People

  • G. L. Bradshaw
  • H. A. Simon
  • P. Langley

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Climbing

Readers

  • Educational Psychology
  • Linear Algebra
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space