Abstract
The input of the learning procedure is a set of data and a set of axioms giving the domains and ranges of elementary functions including predicates. It repeatedly applies these axioms to the input data which yields more and more complex compositions of functions. These compositions are used to form quantified propositions, set constructors, and programs which are composed of the elementary functions in the input. The procedure is controlled by the input data and by partial results such as partial programs which were previously produced. In computer experiments, the procedure classified structured objects such as trains, generated mathematical conjectures such as Goldbach's conjecture, rediscovered laws of physics such as Ohm's law, constructed polygon concepts from line drawings, and developed powerful theorem provers from simple proofs.
This work, in whole or in part, describes components of machines or processes protected by one or more patents or patent applications in Europe, the United States of America, or elsewhere. Further information is available from the author.
This work was supported in part by the German Science Foundation (DFG).
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Ammon, K. (1992). Some experiments with a learning procedure. In: Jantke, K.P. (eds) Analogical and Inductive Inference. AII 1992. Lecture Notes in Computer Science, vol 642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56004-1_6
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DOI: https://doi.org/10.1007/3-540-56004-1_6
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