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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Reasoning about Probabilities in Neural Networks,lly, the combination of knowledge, time, and probability in a connectionist system provides support for integrated knowledge representation and learning in a distributed environment, dealing with the various dimensions of reasoning of an idealised agent [94, 202].

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Conclusions,ational models with integrated reasoning and learning capability, in which neural networks provide the machinery necessary for cognitive computation and learning, while logic provides practical reasoning and explanation capabilities to the neural models, facilitating the necessary interaction with the outside world.

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1611-2482 , slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? ...The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: o

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Applications of Connectionist Nonclassical Reasoning,compares the CML representation of a distributed knowledge representation problem with the representation of the same problem in connectionist intuitionistic logic (CIL), the type of reasoning presented in Chap. 7. We begin with a simple card game, as described in [87].

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Neural-Symbolic Learning Systems,nd knowledge. They did so by comparing the performance of KBANN with other hybrid, neural, and purely symbolic inductive learning systems (see [159, 189] for a comprehensive description of a number of symbolic inductive learning systems, including inductive logic programming systems).

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Connectionist Intuitionistic Reasoning,l, the neural networks can be trained from examples to adapt to new situations using standard neural learning algorithms, thus providing a unifying foundation for intuitionistic reasoning, knowledge representation, and learning.

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Argumentation Frameworks as Neural Networks,lthough symbolic logic-based models have been the standard for the representation of argumentative reasoning [31, 108], such models are intrinsically related to artificial neural networks, as we shall show in this chapter.

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