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Carbonell, J. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In Michalski, R., Carbonell, J., & Mitchell, T. (Eds.) Machine Learning: An Artificial Intelligence Approach. Morgan Kaufman Publishers: San Mateo, CA.

@InBook{,
  ALTauthor = 	 {Jaime Carbonell},
  ALTeditor = 	 {Michalski, R.,
Carbonell, J., & Mitchell, T.},
  title = 	 {Machine Learning: An
Artificial Intelligence Approach},
  chapter = 	 { Derivational analogy: A theory of reconstructive
problem solving and expertise acquisition},
  publisher = 	 {Morgan Kaufman Publishers},
  year = 	 {1986},
  OPTaddress = 	 {San Mateo, CA},
}

Author of the summary: Jim Davies, 2002, jim@jimdavies.org

Cite this paper for:

Four kinds of problem solving: Carbonell 1983: Tranformational Analogy [638]. Took existing plan and modified it at the action level.

Derivational Analogy: Stores the goals and justifications at different levels for the actions taken, to facilitate plan adaptation. I think that's why the term "derivational" is used. Imagine quicksort implemented in Pascal and LISP. Their similarity is at the general specification, not in the actual code. The actual code really wouldn't help.

The derivational trace is stored which contains decisions, justifications, and failure-cause propogations, as well as problems, steps, and the solution.[640]

Also described are ways to get rules out of the cases in memory [644]

Summary author's notes:


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