Abstract
In this paper we describe bolero, a case-based reasoner that learns strategic knowledge (plans) to improve the problem-solving capabilities of an expert system. As a planner, bolero is a reactive planner that when gathering new observations can immediately generate a new plan to cope with the new situations. As a learner, bolero is capable of learning strategies from observation of the problem-solving performed by a teacher. From this experience, bolero plans strategies that solve new problems. bolero learns from success and failure during its problem-solving process. An evaluation to measure the efficiency of bolero is performed by comparing the system's results against both the correct solution of a case and the solutions provided by different domain experts. bolero has been proved useful in acquiring strategic knowledge and in refining existing strategies, in a real-life expert system for pneumonia diagnosis.
Research partially supported by CICYT 801/90 Massive Memory Project and Esprit II 2148 Valid Project.
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© 1991 Springer-Verlag Berlin Heidelberg
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Lopez, B., Plaza, E. (1991). Case-based learning of strategic knowledge. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017033
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DOI: https://doi.org/10.1007/BFb0017033
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