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T. Mitchell, R. Keller, and S. Kedar-Cabelli, Explanation-Based Generalization: A Unifying View. Machine Learning, 1, 1982.

Author of the summary: J. William Murdock, 1997, murdock@cc.gatech.edu

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Keywords: Explanation, Operationality, Learning

Systems: An example uses LEX and there is some discussion of Winston's
ANALOGY and Lebowitz's UNIMEM.

Summary: Contrasts relatively weak "similarity-based generalization"
(standard supervised learning) with "explanation-based" methods that
rely on a powerful domain theory to produce deductively justifiable
concept definitions.  Argues that a single compact, well-defined
algorithm encompasses all of the relevant features of all past work in
explanation based methods.  Presents this algorithm in two steps,
explanation by application of deductive rules and generalization by
variablizing features.  Illustrates the algorithm on the safe-to-stack
and cup problems.  Compares EBG to Winston's ANALOGY program which
also constructs generalizations but goes so using concrete examples
with causal relationships encoded.  Describes an example of learning
search heuristics in LEX (which solves integral calculus problems).
Contrasts this with a variety of single purpose heuristic learning
systems.  Presents different views of EBG (learning, generalization,
operationalized reformulation, etc.).  Discusses open research
problems such as imperfect theories, combining EBG with
similarity-based methods (e.g. UNIMEM which verifies empirical
generalizations by attempting to construct explanations), integrating
EBG into larger problems (discussing LEX2 and METALEX).  In an
appendix, describes DeJong's work on explanation-based generalization
for story understanding.

Summary author's notes:

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