A Structural Theory of Explanation-Based Learning

نویسنده

  • Oren Etzioni
چکیده

The impact of Explanation-Based Learning (EBL) on problem-solving eciency varies greatly from one problem space to another. In fact, seemingly minute modications to problem space encoding can drastically alter EBL's impact. For example, while prodigy/ebl (a state-of-the-art EBL system) signicantly speeds up the prodigy problem solver in the Blocksworld, prodigy/ebl actually slows prodigy down in a representational variant of the Blocksworld constructed by adding a single, carefully chosen, macro-operator to the Blocksworld operator set. Although EBL has been tested experimentally, no theory has been put forth that accounts for such phenomena. This paper presents such a theory. The theory exhibits a correspondence between a graph representation of problem spaces and the proofs used by EBL systems to generate search-control knowledge. The theory relies on this correspondence to account for the variations in EBL's impact. This account is validated by static, a program that extracts EBL-style control knowledge directly from the graph representation, without using training examples. When tested on prodigy/ebl's benchmark tasks, static was up to three times as eective as prodigy/ebl in speeding up prodigy.

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عنوان ژورنال:
  • Artif. Intell.

دوره 60  شماره 

صفحات  -

تاریخ انتشار 1993