Accession Number:

ADA191237

Title:

Hill-Climbing Theories of Learning

Descriptive Note:

Interim rept. Jul 1986-Jul 1987

Corporate Author:

CALIFORNIA UNIV IRVINE SCHOOL OF INFORMATION AND COMPUTER SCIENCE

Report Date:

1987-12-01

Pagination or Media Count:

15.0

Abstract:

Much human learning appears to be gradual and unconscious, suggesting a very limited form of search through the space of hypotheses. We propose hill climbing as a framework for such learning and consider a number of systems that learn in this manner. We focus on CLASSIT, a model of concept formation that incrementally acquires a conceptual hierarchy, and MAGGIE, a model of skill improvement that alters motor schemas in response to errors. Both models integrate the processes of learning and performance. Keywords Artificial intelligence Data processing Computer models Computer applications.

Subject Categories:

  • Psychology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE