Abstract
In this paper, we review our experiences with the BACON project, which has focused on empirical methods for discovering numeric laws. The six successive versions of BACON have employed a variety of discovery methods, some very simple and others quite sophisticated. We examine methods for discovering a functional relation between two numeric terms, including techniques for detecting monotonic trends, finding constant differences, and hill-climbing through a space of parameter values. We also consider methods for discovering complex laws involving many terms, some of which build on techniques for finding two-variable relations. Finally, we introduce the notions of intrinsic properties and common divisors, and examine methods for inferring intrinsic values from symbolic data. In each case, we describe the various techniques in terms of the search required to discover useful laws.
This research was supported by Contract N00014-82-K-0168, NR 049-514, from the Information Sciences Division, Office of Naval Research.
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© 1987 Springer-Verlag Berlin Heidelberg
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Langley, P., Simon, H.A., Bradshaw, G.L. (1987). Heuristics for Empirical Discovery. In: Bolc, L. (eds) Computational Models of Learning. Symbolic Computation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-82742-6_2
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DOI: https://doi.org/10.1007/978-3-642-82742-6_2
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