Investigating Explanation-Based Learning:
Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars....
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1993
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| Schriftenreihe: | The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
120 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism |
| Beschreibung: | 1 Online-Ressource (IX, 438 p) |
| ISBN: | 9781461536024 |
| DOI: | 10.1007/978-1-4615-3602-4 |
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| 520 | |a Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism | ||
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4615-3602-4 |
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| spelling | Investigating Explanation-Based Learning edited by Gerald DeJong Boston, MA Springer US 1993 1 Online-Ressource (IX, 438 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 120 Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Erklärungskomponente (DE-588)4267341-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Maschinelles Lernen (DE-588)4193754-5 s Erklärungskomponente (DE-588)4267341-0 s 2\p DE-604 DeJong, Gerald edt Erscheint auch als Druck-Ausgabe 9781461366003 https://doi.org/10.1007/978-1-4615-3602-4 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Investigating Explanation-Based Learning Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Erklärungskomponente (DE-588)4267341-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4267341-0 (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Investigating Explanation-Based Learning |
| title_auth | Investigating Explanation-Based Learning |
| title_exact_search | Investigating Explanation-Based Learning |
| title_full | Investigating Explanation-Based Learning edited by Gerald DeJong |
| title_fullStr | Investigating Explanation-Based Learning edited by Gerald DeJong |
| title_full_unstemmed | Investigating Explanation-Based Learning edited by Gerald DeJong |
| title_short | Investigating Explanation-Based Learning |
| title_sort | investigating explanation based learning |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Erklärungskomponente (DE-588)4267341-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Erklärungskomponente Maschinelles Lernen Aufsatzsammlung |
| url | https://doi.org/10.1007/978-1-4615-3602-4 |
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