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Compilation of Symbolic Knowledge and Integration with Numeric Knowledge Using Hybrid Systems

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3789)


The development of Artificial Intelligence (AI) research has followed mainly two directions: the use of symbolic and connectionist (artificial neural networks) methods. These two approaches have been applied separately in the solution of problems that require tasks of knowledge acquisition and learning. We present the results of implementing a Neuro-Symbolic Hybrid System (NSHS) that allows unifying these two types of knowledge representation. For this, we have developed a compiler or translator of symbolic rules which takes as an input a group of rules of the type IF ... THEN..., converting them into a connectionist representation. Obtained the compiled artificial neural network this is used as an initial neural network in a learning process that will allow the “refinement” of the knowledge. To prove the refinement of the hybrid approach, we carried out a group of tests that show that it is possible to improve in a connectionist way the symbolic knowledge.


  • Artificial Neural Network
  • Hybrid System
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  • Output Unit
  • Incremental Learning

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  • DOI: 10.1007/11579427_2
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© 2005 Springer-Verlag Berlin Heidelberg

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Sánchez, V.G.C., Salgado, G.R., Villegas, O.O.V., Ortega, J.P., Rendón, A.M. (2005). Compilation of Symbolic Knowledge and Integration with Numeric Knowledge Using Hybrid Systems. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29896-0

  • Online ISBN: 978-3-540-31653-4

  • eBook Packages: Computer ScienceComputer Science (R0)