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Accession Number:
ADA191237
Title:
Hill-Climbing Theories of Learning
Corporate Author:
CALIFORNIA UNIV IRVINE SCHOOL OF INFORMATION AND COMPUTER SCIENCE
Report Date:
1987-12-01
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.
Descriptive Note:
Interim rept. Jul 1986-Jul 1987
Pages:
0015
Distribution Statement:
Approved for public release; distribution is unlimited.
Contract Number:
MDA903-85-C-0324
File Size:
0.97MB
