Abstract
Case-based reasoning (CBR) solves problems by retrieving similar, previously solved problems and reusing their solutions. The case base contains a set of cases, and each case holds knowledge about a problem or a situation, together with its corresponding solution or action. The case base acts as a memory, remembering is achieved using similarity-based retrieval, and the retrieved solutions are reused. Newly solved problems may be retained in the case base and so the memory is able to grow as problem-solving occurs.
CBR reuses remembered experiences, where the experience need not record how the solution was reached, simply that the solution was used for the problem. The reliance on stored experiences means that CBR is particularly relevant in domains that are ill defined, not well understood, or where no underlying theory is available. CBR systems are a useful way to capture corporate memory of human expertise.
The fundamental assumption of CBR is that similar problems have similar solutions: a patient with similar symptoms will have the same diagnosis, the price of a house with similar accommodation and location will be similar, the design for a kitchen with a similar shape and size can be reused, and a journey plan is similar to an earlier trip. A related assumption is that the world is a regular place, and what holds true today will probably be true tomorrow. A further assumption relevant to memory is that situations repeat, because if they do not, there is no point remembering them!
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Massie, S., Wiratunga, N., Craw, S. (2024). Case-Based Reasoning. In: Phung, D., Webb, G.I., Sammut, C. (eds) Encyclopedia of Machine Learning and Data Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7502-7_34-2
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Case-Based Reasoning- Published:
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_34-2
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_34-1