Learning by Failing to Explain

Title: en_US Learning by Failing to Explain
Creator: en_US Hall, Robert Joseph
Date: 2004-10-20T20:02:23Z
Date Available: 2004-10-20T20:02:23Z
Date Issued: en_US 1986-05-01
Identifier: en_US AITR-906
uri: http://hdl.handle.net/1721.1/6850
Abstract: en_US Explanation-based Generalization requires that the learner obtain an explanation of why a precedent exemplifies a concept. It is, therefore, useless if the system fails to find this explanation. However, it is not necessary to give up and resort to purely empirical generalization methods. In fact, the system may already know almost everything it needs to explain the precedent. Learning by Failing to Explain is a method which is able to exploit current knowledge to prune complex precedents, isolating the mysterious parts of the precedent. The idea has two parts: the notion of partially analyzing a precedent to get rid of the parts which are already explainable, and the notion of re-analyzing old rules in terms of new ones, so that more general rules are obtained.
Extent: en_US 140 p.; 15467251 bytes; 5755509 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-906
Subject: en_US learning; en_US explanation; en_US heuristic parsing; en_US design; en_US sgraph grammars; en_US subgraph isomorphism