Affiliations: [a] College of Information Security Engineering, Chengdu University of Information Technology, Chengdu, China
| [b] Faculty of Engineering and Built Environment, School of Engineering, The University of Newcastle, Callaghan, NSW, Australia
| [c] Faculty of Management and Economics, The Technical University of Gdansk, Gdansk, Poland
| [d] College of Automatic Control Engineering, Chengdu University of Information Technology, Chengdu, China
Correspondence:
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Corresponding author. Haoxi Zhang, College of Information Security Engineering, Chengdu University of Information Technology, No. 24 Block 1, Xuefu Road, Chengdu, China. Tel./Fax: +86 28 85966953; E-mail: haoxi@cuit.edu.cn.
Abstract: In this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. It is constructed in a similar fashion of how DNA formed: built up by four essential elements. As the DNA produces phenotypes, the Neural Knowledge DNA carries information and knowledge via its four essential interrelated elements, namely, Networks, Experiences, States, and Actions; which store the detail of the artificial neural networks for training and reusing such knowledge. The novelty of this approach is that it uses previous decisional experience to collect and expand intelligence for future decision making formalized support. The experience based collective computational techniques of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) are used to develop aforesaid decisional sustenance. Together with artificial neural networks and reinforcement learning, the proposed Neural Knowledge DNA is used to catch knowledge of a very simple maze problem, and the results show that our Neural Knowledge DNA is a very promising knowledge representation approach for artificial neural network-based intelligent systems.
