Improving the Cognitive Agent Intelligence by Deep Knowledge Classification

Amine Chemchem (1) , Francois Alin (1) , Michaël Krajecki (1)

1 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804

In this paper, a new idea is developed for improving the agent intelligence. In fact with the presented convolutional neural network (CNN) approach for knowledge classification, the agent will be able to manage its knowledge. This new concept allows the agent to select only the actionable rule class, instead of trying to infer its whole rule base exhaustively. In addition, through this research, we developed a comparative study between the proposed CNN approach and the classical classification approaches. As foreseeable the deep learning method outperforms the others in term of classification accuracy.

Mots clés: Supervised classification method; knowledge management; Deep learning; data mining; cognitive agent

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Système multi-agents [cs.MA]

Amine Chemchem, Francois Alin, Michaël Krajecki. Improving the Cognitive Agent Intelligence by Deep Knowledge Classification. International Journal of Computational Intelligence and Applications, 2019, 18 (01), pp.1950005. ⟨10.1142/S1469026819500056⟩. ⟨hal-02528707⟩