MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Technical Reports (1964 - 2004)
  • View Item
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Technical Reports (1964 - 2004)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Learning by Failing to Explain

Author(s)
Hall, Robert Joseph
Thumbnail
DownloadAITR-906.ps (14.75Mb)
Additional downloads
AITR-906.pdf (5.488Mb)
Metadata
Show full item record
Abstract
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.
Date issued
1986-05-01
URI
http://hdl.handle.net/1721.1/6850
Other identifiers
AITR-906
Series/Report no.
AITR-906
Keywords
learning, explanation, heuristic parsing, design, sgraph grammars, subgraph isomorphism

Collections
  • AI Technical Reports (1964 - 2004)

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.