Explanation-Based Knowledge Acquisition of Schemas in Practical Electronics: A Machine Learning Approach
Abstract
This report describes an AI system that learns electronics concepts from the content of training materials similar to those used in military training in practical electronics. The system is given a series of circuits to learn about; each is described with a circuit diagram and a text expressed in propositional form that explains how the circuit accomplishes a specific function. The system uses a naturalistic domain theory to verify the claims made in the text, and then uses explanation-based learning techniques to generalize the explanation and construct a schema for the circuit that can be used to understand later circuits that include the schematic circuits as subcircuits. The system successfully understands later circuits in terms of the schematic ones, and shows savings in some measures of processing as well, and has implications for the design of technical instructional material. Certain important shortcomings of explanation-based learning and schema concepts become clear with this work, and are discussed in some detail. (rh)
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 12, 1990
- Accession Number
- ADA229122
Entities
People
- John H. Mayer
Organizations
- University of Michigan