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A fuzzy rule-based model of human problem-solving is described. The model is presented in its general form and then adapted to fit data from a simulated fault diagnosis task. The model was able to match 50% of human subjects' actions exactly while using the same rules approximately 70% of the time. Problem-solving rules were selected by the model according to measures of recall, applicability, usefulness, and simplicity. Rules were further discriminated by their use of symptomatic information for pattern recognition or topographic information for information seeking.