Abstract
Much of the work on learning in AI can be viewed as an attempt to understand the problem of generalization in a variety of domains. Much of it has been concept learning—acquiring descriptions of some concept from descriptions of particular examples of the concept. Winston1 and Michalski2 have provided a set of standard heuristics for concept acquisition which are applicable in a wide range of domains. Mitchell3 has formulated generalization as a search through a space of possible descriptions.
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References
P. H. Winston, “Learning structural descriptions from examples”, Ph.D. Thesis, MIT
R. S. Michalski, “ Knowledge acquisition through conceptual clustering: a theoretical framework and an algorithm for partitioning data into conjunctive concepts”, Int. J. of Policy Analysis and Information Systems, 4, 3, 1980.
T. M. Mitchell, “Generalization as search,” Artificial Intelligence, 18,203–226.
Procedure Matcher and Acquirer
K. Van Lehn, “Felicity condition for human skill acquisition: validating an AI-based theory”, Ph.D. thesis, MIT, 1983.
J.-C. Latombe and B. Dufay, “An approach to automatic robot programming based on inductive learning,” Robotics Workshop, MIT, 1983.
D. Angluin and C. H. Smith, “A brief survey of inductive inference”, Technical Report 250, Department of Computer Science, Yale University, 1982.
P. H. Winston, Artificial Intelligence, chapter 12, Addison Wesley, Reading, Mass., 1984.
Angluin and Smith, 1982.
P. H. Winston, 1970.
R. R. Berwick, “Locality principles and the acquisition of syntactic knowledge,” Ph.D. thesis, MIT, 1982.
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© 1987 D. Reidel Publishing Company, Dordrecht, Holland
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Andreae, P. (1987). Constraint Limited Generalization: Acquiring Procedures from Examples. In: Vaina, L.M. (eds) Matters of Intelligence. Synthese Library, vol 188. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3833-5_14
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DOI: https://doi.org/10.1007/978-94-009-3833-5_14
Publisher Name: Springer, Dordrecht
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