Accession Number:

ADA171794

Title:

Learning Object-Level and Meta-Level Knowledge in Expert Systems.

Descriptive Note:

Master's thesis,

Corporate Author:

STANFORD UNIV CA DEPT OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

1985-11-01

Pagination or Media Count:

230.0

Abstract:

A high performance expert system can be built by exploiting machine learning techniques. A learning model has been designed and implemented that is capable of constructing a knowledge base, in the form of rules, from a case library and continuously updating it to accommodate new facts. This model is designed primarily for EMYCIN-like systems in which there is uncertainty about data as well as about the strength of inference and in which the rules chain together to infer complex hypotheses. These features greatly complicate the learning problem. In machine learning, two issues that cannot be overlooked practically are efficiency and noise. A subprogram, called CONDENSER, is designed to remove irrelevant features during learning and improve the efficiency. The noise can be handled by optimizing the result to achieve minimal prediction errors. Another subprogram has been developed to learn meta-level rules which guide the invocation of object-level rules and thus enhance the performance of the expert system using the object-level rules. Using the ideas developed in this work, an expert program called JAUNDICE has been built, which can diagnose the likely disease and mechanisms of a patient with jaundice. Experiments with JAUNDICE show the developed theory and method of learning are effective in a complex and noisy environment where data may be inconsistent, incomplete, and erroneous. Author

Subject Categories:

  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE