Abstract
Abstract: Case‐based reasoning (CBR) is a problem‐solving paradigm where past experiences are used to guide problem‐solving. This paradigm shows a great deal of promise for use in intelligent systems. Recent work in intelligent systems focusing on AIDS prevention reflects a growing interest in the case‐based paradigm because AIDS prevention experts rely heavily on memory of previous cases when assessing subjects that exhibit AIDS‐risky behaviors. If an AIDS prevention expert has seen a subject with similar AIDS‐risky behavior previously, he or she is likely to draw on that experience to propose a solution to the new case at hand. This paper describes a CBR system that functions as an AIDS prevention expert. The inputs are risk behavior descriptions and the subject's test results. The system employs fuzzy mathematical algorithms to retrieve and select previous cases, thereby assessing the subject's risk.
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