P. Smolensky, G. Legendre, and Y. Miyata. Integrating Connectionist and Symbolic Computation for the Theory of Language. In V. Honavar and L. Uhr, editors, Artificial Intelligence and Neural Networks: Steps toward Principled Integration, pages 509--530. Academic Press, 1994. Home/Search Document Details and Download
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Heterogeneous Knowledge Representation: integrating.. - Danilo Montesi (1995) (Correct)
....languages (expressed as Horn clause language) with neural networks (expressed as constraints) The importance of a clear and sound integration between the two paradigms is obvious. The integration of these paradigms can succeed in solving complex problems that require heterogeneous knowledge bases [19]. There are two importantpoints in this plan. The former is to use the well known paradigm of constraint logic programming and the already developed results [8] The latter is that neural networks can be trained and tested as a stand alone component and then plugged into a rule system and ....
P. Smolensky, G. Legendre, and Y. Miyata. Integrating Connectionist and Symbolic Computation for the Theory of Language. In V. Honavar and L. Uhr, editors, Artificial Intelligence and Neural Networks: Steps toward Principled Integration, pages 509--530. Academic Press, 1994.
Symbolic Artificial Intelligence And Numeric Artificial Neural.. - Honavar (1994) (4 citations) (Correct)
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Smolensky, P., Legendre, G., and Miyata, Y. (1994). Integrating Connectionist and Symbolic Computation for the Theory of Language. In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. Honavar, V. and Uhr, L. (Ed.) San Diego, CA: Academic Press.
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