Paper Info
Title
Compilation of symbolic knowledge and integration with numeric knowledge using hybrid systems
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.
Year
DOI
Venue
2005
10.1007/11579427_2
MICAI
Keywords
Field
DocType
symbolic rule,hybrid system,neuro-symbolic hybrid system,knowledge representation,artificial intelligence,numeric knowledge,hybrid approach,symbolic knowledge,knowledge acquisition,initial neural network,artificial neural network,connectionist representation,artificial intelligent,neural network
DUAL (cognitive architecture),Descriptive knowledge,Knowledge representation and reasoning,Neuro-fuzzy,Computer science,Artificial intelligence,Artificial neural network,Hybrid system,Connectionism,Machine learning,Knowledge acquisition
Conference
Volume
ISSN
ISBN
3789
0302-9743
3-540-29896-7
Citations 
PageRank 
References 
2
0.39
4
Authors
5
Name
Order
Citations
PageRank
Vianey Guadalupe Cruz Sánchez1277.24
Gerardo Reyes Salgado2144.89
Osslan Osiris Vergara Villegas3156.40
Joaquín Perez-Ortega431.75
Azucena Montes Rendón532.77