Capitalizing on Failure through Case-Based Inference

Abstract

Previous failures to solve problems can be a powerful aid in helping a problem solver to improve. When prior cases in which an error was made are recalled (e.g. common sense mediation of everyday disputes and menu planning), the reasoner may consider whether the same potential for error exists in the new case. As a result, reasoning is directed to that part of the current problem that was responsible for the previous error, sometimes changing the problem solver's focus. Focus may also be directed toward gathering knowledge to evaluate the potential for error in the current case. A case with an error may also suggest a correct solution for the new problem. The combination of these helps the problem solver to avoid repeating mistakes and suggests shortcuts in reasoning that avoid the trial and error of previous cases. Keywords: Artificial intelligence; Cognitive science; Knowledge theory; Man machine systems.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA190927

Entities

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  • Janet L. Kolodner

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  • Georgia Tech

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  • Engineered Resilient Systems
  • Human Systems

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  • Artificial Intelligence
  • Classification
  • Cognitive Science
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  • Artificial Intelligence
  • Systems Analysis and Design

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  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms