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Titlebook: Investigating Explanation-Based Learning; Gerald DeJong Book 1993 Springer Science+Business Media New York 1993 artificial intelligence.kn

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發(fā)表于 2025-3-21 19:02:51 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Investigating Explanation-Based Learning
編輯Gerald DeJong
視頻videohttp://file.papertrans.cn/475/474750/474750.mp4
叢書名稱The Springer International Series in Engineering and Computer Science
圖書封面Titlebook: Investigating Explanation-Based Learning;  Gerald DeJong Book 1993 Springer Science+Business Media New York 1993 artificial intelligence.kn
描述Explanation-Based Learning (EBL) can generally be viewed assubstituting background knowledge for the large training set ofexemplars needed by conventional or empirical machine learningsystems. The background knowledge is used automatically to constructan explanation of a few training exemplars. The learned concept isgeneralized directly from this explanation. .The first EBL systems of the modern era were Mitchell‘s LEX2, Silver‘sLP, and De Jong‘s KIDNAP natural language system. Two of thesesystems, Mitchell‘s and De Jong‘s, have led to extensive follow-upresearch in EBL. This book outlines the significant steps in EBLresearch of the Illinois group under De Jong. .This volume describes theoretical research and computer systems thatuse 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 doneconsciously to avoid sacrificing the ultimate research significance infavor of the expediency of any particular formalism. .The ultimate goal, of course, is to adopt (or devise) the rightformalism. .
出版日期Book 1993
關鍵詞artificial intelligence; knowledge; learning; machine learning; natural language; robot
版次1
doihttps://doi.org/10.1007/978-1-4615-3602-4
isbn_softcover978-1-4613-6600-3
isbn_ebook978-1-4615-3602-4Series ISSN 0893-3405
issn_series 0893-3405
copyrightSpringer Science+Business Media New York 1993
The information of publication is updating

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沙發(fā)
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板凳
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Generalizing Explanation Structures,judged not to be as crucial or as challenging as the other two. If the EBL approach cannot overcome the demands of temporal and number generalization, the system profits little from disjunctive augmentation.
地板
發(fā)表于 2025-3-22 05:24:26 | 只看該作者
,Case Study 3 — PHYSICS 101: Learning in Mathematically Based Domains,nce in symbolic mathematical manipulation. Furthermore, since mathematics is the underlying formal language, many important domain concepts can be adequately captured only through a mathematical specification. Thus, concept learning in these domains is also rooted in mathematics.
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Book 1993onal or empirical machine learningsystems. The background knowledge is used automatically to constructan explanation of a few training exemplars. The learned concept isgeneralized directly from this explanation. .The first EBL systems of the modern era were Mitchell‘s LEX2, Silver‘sLP, and De Jong‘s
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The Springer International Series in Engineering and Computer Sciencehttp://image.papertrans.cn/i/image/474750.jpg
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Some Aspects of Operationality,Operationality has proven to be a troublesome facet of EBL. Two complementary approaches to the operationality problem are presented here. The first involves constructing and remembering partial generalizations called . in addition to the standard EBL generalization. The second comprises a theory of ..
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發(fā)表于 2025-3-23 07:39:33 | 只看該作者
,Case Study 1 — ARMS: Acquiring Robotic Assembly Plans,The ARMS system is an . operating in a robot assembly domain. ARMS is a . because it learns, in part, by analyzing an external agent’s solution to a given problem, as opposed to a solution produced by an internal weak-method problem solver.
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