Case-Based Reasoning: A Research Paradigm

Authors

  • Stephen Slade

DOI:

https://doi.org/10.1609/aimag.v12i1.883

Abstract

Expertise comprises experience. In solving a new problem, we rely on past episodes. We need to remember what plans succeed and what plans fail. We need to know how to modify an old plan to fit a new situation. Case-based reasoning is a general paradigm for reasoning from experience. It assumes a memory model for representing, indexing, and organizing past cases and a process model for retrieving and modifying old cases and assimilating new ones. Case-based reasoning provides a scientific cognitive model. The research issues for case-based reasoning include the representation of episodic knowledge, memory organization, indexing, case modification, and learning. In addition, computer implementations of case-based reasoning address many of the technological shortcomings of standard rule-based expert systems. These engineering concerns include knowledge acquisition and robustness. In this article, I review the history of case-based reasoning, including research conducted at the Yale AI Project and elsewhere.

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Published

1991-03-15

How to Cite

Slade, S. (1991). Case-Based Reasoning: A Research Paradigm. AI Magazine, 12(1), 42. https://doi.org/10.1609/aimag.v12i1.883

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Section

Articles