Abstract
One of the major problems in the field of theorem proving is the reduction of the huge, often exponential, search space. Up to now there exist no theories for selecting the optimal search strategy (heuristics) for a given formula in general. We will describe two different approaches using back propagation networks for learning global and local heuristics. First experiments and promising results for a theorem prover for propositional logic connected to a back propagation network will be shown.
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© 1989 Springer-Verlag Berlin Heidelberg
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Ertel, W., Schumann, J.M.P., Suttner, C.B. (1989). Learning Heuristics for a Theorem Prover using Back Propagation. In: Retti, J., Leidlmair, K. (eds) 5. Österreichische Artificial-Intelligence-Tagung. Informatik-Fachberichte, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-74688-8_10
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DOI: https://doi.org/10.1007/978-3-642-74688-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-51039-0
Online ISBN: 978-3-642-74688-8
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