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SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference

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Perspectives of Neural-Symbolic Integration

Part of the book series: Studies in Computational Intelligence ((SCI,volume 77))

The ability to reason effectively with a large body of knowledge is a cornerstone of human intelligence. Consequently, the development of efficient, largescale reasoning systems has been a central research goal in computer science and artificial intelligence. Although there has been notable progress toward this goal, an efficient, large-scale reasoning system has remained elusive. Given that the human brain is the only extant system capable of supporting a broad range of efficient, large-scale reasoning, it seems reasonable to expect that an understanding of how the brain represents knowledge and performs inferences might lead to critical insights into the structure and design of large-scale inference syste

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Author information

Authors and Affiliations

  1. International Computer Science Institute, 1947 Center Street, Suite 600, 94704, Berkeley, CA, USA

    Lokendra Shastri

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  1. Lokendra Shastri
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Editor information

Editors and Affiliations

  1. University Clausthal, Julius Albert Strabe 4, 38678, Clausthal-Zellerfeld, Germany

    Barbara Hammer

  2. University of Karlsruhe, 76128, Karlsruhe, Germany

    Pascal Hitzler

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Shastri, L. (2007). SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference. In: Hammer, B., Hitzler, P. (eds) Perspectives of Neural-Symbolic Integration. Studies in Computational Intelligence, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73954-8_8

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  • DOI: https://doi.org/10.1007/978-3-540-73954-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73953-1

  • Online ISBN: 978-3-540-73954-8

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