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

Title: en_US Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology
Creator: en_US Resnick, Paul
Date: 2004-10-20T20:00:53Z
Date Available: 2004-10-20T20:00:53Z
Date Issued: en_US 1989-02-01
Identifier: en_US AITR-1052
uri: http://hdl.handle.net/1721.1/6836
Abstract: en_US 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.
Extent: en_US 101 p.; 11635658 bytes; 4564645 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-1052
Subject: en_US learning; en_US explanation-based learning; en_US model-basedstroubleshooting