The human brain possesses the remarkable capability of understanding, interpreting, and producing language, structures, and logic. Unlike their biological counterparts, artificial neural networks do not form such a close liason with symbolic reasoning: logic-based inference mechanisms and statistical machine learning constitute two major and very different paradigms in artificial intelligence with complementary strengths and weaknesses. Modern application scenarios in robotics, bioinformatics, language processing, etc., however require both the efficiency and noise-tolerance of statistical models and the generalization ability and high-level modelling of structural inference meachanisms. A variety of approaches has therefore been proposed for combining the two paradigms.
Perspectives of Neural-Symbolic Integration
Barbara Hammer & Pascal Hitzler (Edit.)
Perspectives of Neural-Symbolic Integration
Barbara Hammer & Pascal Hitzler (Edit.)
Springer 2007 PDF 320 pages 5.07 MB
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