Towards Neural Knowledge DNA
Haoxi Zhang, Cesar Sanin, Edward Szczerbicki

TL;DR
This paper introduces Neural Knowledge DNA, a novel framework inspired by biological DNA, aimed at enhancing knowledge representation and sharing among machines through four core elements.
Contribution
It proposes a new framework that adapts neural network principles to knowledge representation, facilitating knowledge discovery, storage, reuse, and sharing.
Findings
Framework supports knowledge sharing among machines
Enables storing and reusing knowledge efficiently
Inspired by biological DNA structure
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 dedicate 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 organisation. 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 elements, namely, Networks, Experiences, States, and Actions.
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Taxonomy
TopicsReinforcement Learning in Robotics · Modular Robots and Swarm Intelligence · Evolutionary Algorithms and Applications
