Abstract
Though they constitute the major knowledge source in problem-solving systems, no unified theory of heuristics has emerged. Pearl [15] defines heuristics as “criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal”. The absence of a more precise definition has impeded our efforts to understand, utilize, and discover heuristics. Another consequence is that problem-solving techniques which rely on heuristic knowledge cannot be relied upon to act rationally — in the sense of the normative theory of rationality.
To provide a sound basis for BPS, the Bayesian Problem-Solver, we have developed a simple formal theory of heuristics, which is general enough to subsume traditional heuristic functions as well as other forms of problem-solving knowledge, and to straddle disparate problem domains. Probabilistic heuristic estimates represent a probabilistic association of sensations with prior experience — specifically, a mapping from observations directly to subjective probabilities which enables the use of theoretically principled mechanisms for coherent inference and decision making during problem-solving. This paper discusses some of the implications of this theory, and describes its successful application in BPS.
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This research was made possible by support from Heuristicrats, the National Aeronautics and Space Administration, and the Rand Corporation.
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Hansson, O., Mayer, A. Probabilistic heuristic estimates. Ann Math Artif Intell 2, 209–220 (1990). https://doi.org/10.1007/BF01531007
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DOI: https://doi.org/10.1007/BF01531007