@article{Slade_1991, title={Case-Based Reasoning: A Research Paradigm}, volume={12}, url={https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/883}, DOI={10.1609/aimag.v12i1.883}, abstractNote={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.}, number={1}, journal={AI Magazine}, author={Slade, Stephen}, year={1991}, month={Mar.}, pages={42} }