Selectively Generalizing Plans for Problem-Solving

  title={Selectively Generalizing Plans for Problem-Solving},
  author={Steven Minton},
Abst ract Problem solving programs that generalize and save plans in order to improve their subsequent performance inevitably face the danger of being overwhelmed by an ever-increasing number of stored plans. To cope with this problem, methods must be developed for selectively learning only the most valuable aspects of a new plan. This paper describes MORRIS, a heuristic problem solver that measures the utility of plan fragments to determine whether they are worth learning. MORRIS generalizes… CONTINUE READING

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Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics

  • T. Mitchell, P. Utgoff, R. Banerjl
  • In Machine Learning,
  • 1983
1 Excerpt

GPS: A Case Study in Generality and Problem Solving

  • G. Ernst, Newell
  • 1969
1 Excerpt

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