Improving the Cognitive Agent Intelligence by Deep Knowledge Classification

Author:

Chemchem Amine1,Alin François1,Krajecki Michael1

Affiliation:

1. CReSTIC Center, University of Reims Champagne-Ardenne, Campus Moulin de la Housse BP 1039, 51687 Reims Cedex 2, France

Abstract

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Science Applications,Theoretical Computer Science,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards a Hierarchical Deep Learning Approach for Intrusion Detection;Machine Learning for Networking;2020

2. Physics of Mind – A Cognitive Approach to Intelligent Control Theory;Cyber-Physical Systems and Control;2019-11-30

3. Combining SMOTE Sampling and Machine Learning for Forecasting Wheat Yields in France;2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE);2019-06

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