Efficient Specialization of Relational Concepts

Abstract

An algorithm is presented for a common induction problem, the specialization of overly general concepts. A concept is too general when it matches a negative example. The particular case addressed here assumes that concepts are represented as conjunctions of positive literals, that specialization is performed by conjoining literals to the overly general concept, and that the resulting specializations are to be as general as possible. Although the problem is NP-hard, there exists an algorithm, based on manipulation of bit vectors, that has provided good performance in practice. Keywords: Strategy discovery; Skill acquisition; Machine learning; Cognitive science; Impasses-driven learning; Artificial intelligence.

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Document Details

Document Type
Technical Report
Publication Date
Mar 10, 1989
Accession Number
ADA218889

Entities

People

  • Kurt VanLehn

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Cognitive Science
  • Computer Science
  • Computer Vision
  • Computers
  • Coverings
  • Discrimination
  • Language
  • Learning
  • Machine Learning
  • Military Research
  • New York
  • Psychology
  • United States
  • Universities

Fields of Study

  • Education

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence

Technology Areas

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