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Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology

dc.date.accessioned2004-10-20T20:00:53Z
dc.date.accessioned2018-11-24T10:22:04Z
dc.date.available2004-10-20T20:00:53Z
dc.date.available2018-11-24T10:22:04Z
dc.date.issued1989-02-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6836
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/6836
dc.description.abstractThis 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.extent101 p.en_US
dc.format.extent11635658 bytes
dc.format.extent4564645 bytes
dc.language.isoen_US
dc.subjectlearningen_US
dc.subjectexplanation-based learningen_US
dc.subjectmodel-basedstroubleshootingen_US
dc.titleGeneralizing on Multiple Grounds: Performance Learning in Model-Based Technologyen_US


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