Abstract
Today’s expert systems have no ability to learn from experience. This commonly heard criticism, unfortunately, is largely true. Except for simple classification systems, expert systems do not employ a learning component to construct parts of their knowledge bases from libraries of previously solved cases. And none that I know of couples learning into closedloop modification based on experience, although the SOAR architecture [Rosenbloom and Newell 1985] comes the closest to being the sort of integrated system needed for continuous learning. Learning capabilities are needed for intelligent systems that can remain useful in the face of changing environments or changing standards of expertise. Why are the learning methods we know how to implement not being used to build or maintain expert systems in the commercial world?
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© 1989 Kluwer Academic Publishers, Boston
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Buchanan, B.G. (1989). Can Machine Learning Offer Anything to Expert Systems?. In: Marcus, S. (eds) Knowledge Acquisition: Selected Research and Commentary. Machine Learning, vol 92. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1531-5_2
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DOI: https://doi.org/10.1007/978-1-4613-1531-5_2
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