An apprentice-based approach to knowledge acquisition

Sridhar Mahadevan, Tom M. Mitchell, Jack Mostow, Lou Steinberg, Prasad V. Tadepalli

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We explore here the feasibility of learning apprentice programs: interactive knowledge-based assistants that learn by observing and analyzing the problem-solving steps of their users. In particular, we describe a learning apprentice for digital circuit design, called LEAP. LEAP learns feasible ways of decomposing circuit modules into submodules, as well as the recommended method when there are competing feasible decompositions. VBL is an explanation-based learning technique used in LEAP to infer problem-reduction operators for decomposing circuit modules. PED is a general extension of explanation-based learning to incomplete domain theories containing determinations. PED is used in LEAP to learn control rules for ranking alternative decompositions as well as to extend LEAP's partial theory of circuit cost. An experimental study shows that by using this approach LEAP can learn a significant subset of a manually created knowledge base for boolean circuit design. The experimental study also reveals some limitations of LEAP, and more generally suggests directions for further research in building effective learning apprentice systems.

Original languageEnglish (US)
Pages (from-to)1-52
Number of pages52
JournalArtificial Intelligence
Volume64
Issue number1
DOIs
StatePublished - Nov 1993

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

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