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Neural Network Models for Abduction Problems Solving

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4692))

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Abstract

Due to its’ connectionist nature, abductive reasoning may get neural network implementations that yet require structure adaptation to the abduction problems which Bylander and the team asserted. The paper proposes neural models for all known abduction problems, in a really unified manner, and with a sound and straightforward embedding in the existing neural network paradigms.

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References

  1. Ariton, V., Ariton, D.: A General Approach for Diagnostic Problems Solving by Abduction. In: Proc. of IFAC-SAFEPROCESS, Budapest, Hungary, pp. 446–451 (2000)

    Google Scholar 

  2. Ayeb, B., Wang, S., Ge, J.: A Unified Model for Abduction-Based Reasoning. IEEE Trans. on Systems Man and Cybernetics - Part A: Systems and Humans 28(4), 408–424 (1998)

    Article  Google Scholar 

  3. Bylander, T., Allemang, D., Tanner, M.C., Josephson, J.R.: The Computational Complexity of Abduction. Artificial Intelligence 49, 25–60 (1991)

    Article  MATH  Google Scholar 

  4. Goel, A., Ramanujam, J.: A Neural Architecture for a Class of Abduction Problems. IEEE Transactions on Systems Man and Cybernetics 26(6), 854–860 (1996)

    Article  Google Scholar 

  5. Peng, Y., Reggia, J.: Abductive Inference Models for Diagnostic Problem Solving. Springer, Heidelberg (1990)

    MATH  Google Scholar 

  6. Wang, S., Ayeb, B.: Diagnosis: Hypothetical Reasoning With A Competition-Based Neural Architecture. In: Proc. International Joint Conference on Neural Networks, vol. I, pp. 7–12 (1992)

    Google Scholar 

  7. Xu, Y., Zhang, C.: An improved Critical Diagnosis Reasoning Method. In: ICTAI, Toulouse, France, vol. 1, pp. 170–173 (1996)

    Google Scholar 

  8. Zell, A., Mache, N., Sommer, T., Korb, T.: SNNS- Neural Network Simulator, User Manual. University of Tuebingen (1991)

    Google Scholar 

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Author information

Authors and Affiliations

  1. “Danubius” University, Lunca Siretului no. 3, 800416, Galati, Romania

    Viorel Ariton

  2. “Dunarea de Jos” University, Domneasca no. 47, 800001, Galati, Romania

    Doinita Ariton

Authors
  1. Viorel Ariton
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  2. Doinita Ariton
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Editor information

Bruno Apolloni Robert J. Howlett Lakhmi Jain

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© 2007 Springer-Verlag Berlin Heidelberg

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Ariton, V., Ariton, D. (2007). Neural Network Models for Abduction Problems Solving. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_7

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  • DOI: https://doi.org/10.1007/978-3-540-74819-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74817-5

  • Online ISBN: 978-3-540-74819-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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