This thesis explores ways to augment a model-based diagnostic program with a learning component, so that it speeds up as it solves problems. Several learning components are proposed, each exploiting a different kind of similarity between diagnostic examples. Through analysis and experiments, we explore the effect each learning component has on the performance of a model-based diagnostic program. We also analyze more abstractly the performance effects of Explanation-Based Generalization, a technology that is used in several of the proposed learning components.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6836 |
| Date | 01 February 1989 |
| Creators | Resnick, Paul |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 101 p., 11635658 bytes, 4564645 bytes, application/postscript, application/pdf |
| Relation | AITR-1052 |
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