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CIM — The Hybrid Symbolic/Connectionist Rule-Based Inference System

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Intelligent Problem Solving. Methodologies and Approaches (IEA/AIE 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1821))

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Abstract

Previous research has shown that connectionist models are suitable for cognitive and natural language processing tasks. An inference mechanism is a key element in commonsense reasoning in a natural language understanding system. This research project offers a connectionist alternative to Buchheit’s symbolic inference module for INFANT called the Connectionist Inference Mechanism (CIM). CIM is a hybrid cognitive model that combines the advantages of the symbolic approach, local representation, and parallel distributed processing. Moreover, it makes good use of its modular structure. Several modules work together in CIM, including memory, neural networks, and a binding set, to perform the inference generation. Besides rule application capability, CIM is also able to perform variable binding. A number of experiments have shown that CIM can make inferences appropriately.

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References

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Rd., Ladkrabang, Bangkok, 10520, Thailand

    Pattarachai Lalitrojwong

Authors
  1. Pattarachai Lalitrojwong
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Editors and Affiliations

  1. The Center for Advanced Computer Studies, University of Lousiana, 2 Rex Street, Lafayette, LA, 70504-4330, USA

    Rasiah Logananthara

  2. Department of Neural Information Processing, University of Ulm, Oberer Eselsberg, 89069, Ulm, Germany

    Günther Palm

  3. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666-4616, USA

    Moonis Ali

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© 2000 Springer-Verlag Berlin Heidelberg

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Lalitrojwong, P. (2000). CIM — The Hybrid Symbolic/Connectionist Rule-Based Inference System. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_65

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  • DOI: https://doi.org/10.1007/3-540-45049-1_65

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67689-8

  • Online ISBN: 978-3-540-45049-8

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