Perspectives of Neural-Symbolic Integration

Hammer, Barbara|Hitzler, Pascal

ISBN 10: 354073953X / ISBN 13: 9783540739531

Editore: Springer Berlin Heidelberg, 2007

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Presents recent developments in neural-symbolic integrationWhen it comes to robotics and bioinformatics, the Holy Grail everyone is seeking is how to dovetail logic-based inference and statistical machine learning. This volume offers some poss.

Codice articolo 4899830

Dati bibliografici

• Titolo: Perspectives of Neural-Symbolic Integration
• Editore: Springer Berlin Heidelberg
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 354073953X
• ISBN 13: 9783540739531
• Rilegatura: Gebunden
• Condizione: New

Descrizione articolo

Riassunto

When it comes to robotics and bioinformatics, the Holy Grail everyone is seeking is how to dovetail logic-based inference and statistical machine learning. This volume offers some possible solutions to this eternal problem. Edited with flair and sensitivity by Hammer and Hitzler, the book contains state-of-the-art contributions in neural-symbolic integration, covering `loose' coupling by means of structure kernels or recursive models as well as `strong' coupling of logic and neural networks.

Dalla quarta di copertina

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.

This carefully edited volume contains state-of-the-art contributions in neural-symbolic integration, covering `loose' coupling by means of structure kernels or recursive models as well as `strong' coupling of logic and neural networks. It brings together a representative selection of results presented by some of the top researchers in the field, covering theoretical foundations, algorithmic design, and state-of-the-art applications in robotics and bioinformatics.