Knowledge Equivalence in Digital Twins of Intelligent Systems

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.Comment: 35 pages, 16 figures. Under review. Submitted to ACM Transactions on Modeling and Computer Simulation (TOMACS

Modelling Environments for Distributed Simulation

Abstract. Decentralised, event-driven distributed simulation is particularly suitable for modelling systems with inherent asynchronous parallelism, such as agentbased systems. However the efficient simulation of multi-agent systems presents particular challenges which are not addressed by standard parallel discrete event simulation (PDES) models and techniques. PDES approaches based on the logical process paradigm assume a fixed decomposition into processes, each of which maintains its own portion of the state of the simulation. The interaction between the processes is fixed in advance and does not change during the simulation. In contrast, simulations of MAS typically have a large shared state, the agents ’ environment, which is only loosely associated with any particular process. In this paper, we present a model of the shared state of a distributed MAS simulation of situated agents. We consider the problems of efficient sensing, parallel actions and action conflicts, and present preliminary work on an approach to the simulation of the environment which addresses these issues.