Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology
Data(s) |
20/10/2004
20/10/2004
01/02/1989
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Resumo |
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. |
Formato |
101 p. 11635658 bytes 4564645 bytes application/postscript application/pdf |
Identificador |
AITR-1052 |
Idioma(s) |
en_US |
Relação |
AITR-1052 |
Palavras-Chave | #learning #explanation-based learning #model-basedstroubleshooting |