Knowledge Acquisition from Structural Descriptions.

Frederick Hayes-Roth, J. McDermott

Expert InsightsPublished 1976

Discusses the representation of concepts and antecedent-consequent productions, and describes a method for inducing knowledge by abstracting such representations from a sequence of training examples. The proposed learning method, interference matching, induces abstractions by finding relational properties common to two or more exemplars. Three tasks solved by a program that performs an interference-matching algorithm are presented. Several problems concerning the relational representation of examples and the induction of knowledge by interference matching are also discussed. The similarities between this task and other computer science problems are indicated, and directions for future research are considered. 64 pp. Ref.

Document Details

  • Copyright: RAND Corporation
  • Availability: Web Only
  • Year: 1976
  • Pages: 64
  • Document Number: P-5910

Citation

Chicago Manual of Style

Hayes-Roth, Frederick and J. McDermott, Knowledge Acquisition from Structural Descriptions. Santa Monica, CA: RAND Corporation, 1976. https://www.rand.org/pubs/papers/P5910.html.
BibTeX RIS

This publication is part of the RAND paper series. The paper series was a product of RAND from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.