Knowledge Equivalence in Digital Twins of Intelligent Systems

TL;DR

This paper introduces a novel concept of knowledge equivalence for digital twins of intelligent systems, proposing methods to maintain model accuracy by synchronizing knowledge, which improves simulation reliability and efficiency.

Contribution

It presents a new approach for maintaining knowledge equivalence in digital twins of intelligent systems, addressing challenges in synchronizing knowledge and reducing update overhead.

Findings

Knowledge equivalence can tolerate deviations, reducing unnecessary updates.

The approach improves the trade-off between update overhead and simulation reliability.

Quantitative analysis confirms the effectiveness of the proposed method.

Abstract

A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The paper focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This paper proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the trade-off between update overhead and simulation reliability.