Titlebook: Neural-Symbolic Cognitive Reasoning; Artur S. d’Avila Garcez,Luís C. Lamb,Dov M. Gabbay Textbook 2009 Springer-Verlag Berlin Heidelberg 20

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Neural-Symbolic Learning Systems,ic systems have become a very active area of research in the last decade. The integration of neural networks and symbolic knowledge was already receiving considerable attention in the 1990s. For instance, in [250], Towell and Shavlik presented the influential model KBANN (Knowledge-Based Artificial

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Connectionist Intuitionistic Reasoning,to represent intuitionistic theories, and show that for each intuitionistic theory and intuitionistic modal theory, there exists a corresponding neural-network ensemble that computes a fixed-point semantics of the theory. This provides a massively parallel model for intuitionistic reasoning. As usua

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Fibring Neural Networks,ed) into a system capable of dealing with the different dimensions of a reasoning agent. In this chapter, we introduce a methodology for combining neural-network architectures based on the idea of fibring logical systems [101]. Fibring allows one to combine different logical systems in a principled

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