Automatic Concept Formation in a Rich Input Domain

Abstract

Learning by observation involves the creation of categories summarizing experience. In this research note, we summarize our research during the contact period with UNIMEM, an Artificial Intelligence system that learns by observation. UNIMEM is a robust computer program that can be run on many domains with real-world problem characteristics, such as uncertainty, incompleteness, and large numbers of examples. We give an overview of the program that illustrates UNIMEM's key elements, including the automatic creation of non- disjoint concept hierarchies that are evaluated over time. We then describe several experiments that we have carried out with UNIMEM, testing it on different domains (universities, Congressional voting records, and terrorist events), and an examination of the effect of varying UNIMEM's parameters on the resulting concept hierarchies. Finally, we discuss future directions for our work with the program. Keywords: Expert systems, Reasoning, UNIMEM(Universal Memory).

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1988
Accession Number
ADA196719

Entities

People

  • Michael Lebowitz

Organizations

  • Columbia University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Energy and Power Technologies
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automatic
  • Classification
  • Cognitive Science
  • Computer Programs
  • Computer Science
  • Hierarchies
  • Information Processing
  • Information Systems
  • Machine Learning
  • Observation
  • Psychology
  • Social Sciences
  • Students
  • Terrorists
  • Universities

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML