Compilation of symbolic knowledge and integration with numeric knowledge using hybrid systems

  • Authors:

  • Vianey Guadalupe Cruz Sánchez;Gerardo Reyes Salgado;Osslan Osiris Vergara Villegas;Joaquín Perez Ortega;Azucena Montes Rendón

  • Affiliations:

  • Centro Nacional de Investigación y Desarrollo Tecnológico (cenidet), Computer Science Department, Cuernavaca, Morelos, México;Centro Nacional de Investigación y Desarrollo Tecnológico (cenidet), Computer Science Department, Cuernavaca, Morelos, México;Centro Nacional de Investigación y Desarrollo Tecnológico (cenidet), Computer Science Department, Cuernavaca, Morelos, México;Centro Nacional de Investigación y Desarrollo Tecnológico (cenidet), Computer Science Department, Cuernavaca, Morelos, México;Centro Nacional de Investigación y Desarrollo Tecnológico (cenidet), Computer Science Department, Cuernavaca, Morelos, México

  • Venue:
  • MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
  • Year:
  • 2005

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Abstract

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.