dc.date.accessioned | 2004-10-20T20:00:53Z | |
dc.date.accessioned | 2018-11-24T10:22:04Z | |
dc.date.available | 2004-10-20T20:00:53Z | |
dc.date.available | 2018-11-24T10:22:04Z | |
dc.date.issued | 1989-02-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/6836 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/6836 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 101 p. | en_US |
dc.format.extent | 11635658 bytes | |
dc.format.extent | 4564645 bytes | |
dc.language.iso | en_US | |
dc.subject | learning | en_US |
dc.subject | explanation-based learning | en_US |
dc.subject | model-basedstroubleshooting | en_US |
dc.title | Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology | en_US |