Experiments of A neuro symbolic deep learning system with incomplete data

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Presented at CRIS 2020 by

Jihane Boulahia

In this paper, we discuss the properties of a deep learning hybrid learning system called DHLS (Deep Hybrid Learning System) proposed by J. - Boulahia Smirani. The DHLS system has modules capable of performing a bidirectional transfer of information between a symbolic module and a deep learning module. We present various experiments that demonstrate various strengths of the DHLS system are : the ability to integrate theoretical knowledge ( rules) and empirical knowledge (examples) , the ability to make an initial knowledge base ( rules) of the converted into a connectionist network , using empirical knowledge by learning help revise the knowledge , acquire new knowledge and explain these new knowledge and finally the ability to improve the performance of systems or simple symbolic connectionist Keywords Deep learning hybrid system, neural networks, integration of symbolic rules, extraction of symbolic rules.