A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems
arXiv:2608.11221v1 Announce Type: new Abstract: Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The behaviour of these systems emerges from the interaction between those artefacts and their operational environment. Simulation and co-simulation have become essential approaches for analysing CPS behaviour and, through simulation campaigns, developers can explore system responses under changing conditions, including interactions with the environment. However, the lack of details and understanding of some environmentmediated interactions (typically the ones beyond direct sensing and actuation), which remain unmodelled due to their complexity, a lack of time, or a lack of domain experience, hinders the proper comprehension and exploitation of simulation results. To address these limitations, we propose a conceptual framework leveraging the novel concept of Influences to support the iterative and incremental refinement of simulation campaigns and deepen the understanding of the system behaviour. We demonstrate the proposed approach through a case study involving a mobile robot implemented using Simulink/Gazebo co-simulation.
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[Submitted on 22 Jul 2026]
Title:A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems
View a PDF of the paper titled A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems, by Barbara da Silva Oliveira (UniCA and 8 other authors
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Abstract:Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The behaviour of these systems emerges from the interaction between those artefacts and their operational environment. Simulation and co-simulation have become essential approaches for analysing CPS behaviour and, through simulation campaigns, developers can explore system responses under changing conditions, including interactions with the environment. However, the lack of details and understanding of some environmentmediated interactions (typically the ones beyond direct sensing and actuation), which remain unmodelled due to their complexity, a lack of time, or a lack of domain experience, hinders the proper comprehension and exploitation of simulation results. To address these limitations, we propose a conceptual framework leveraging the novel concept of Influences to support the iterative and incremental refinement of simulation campaigns and deepen the understanding of the system behaviour. We demonstrate the proposed approach through a case study involving a mobile robot implemented using Simulink/Gazebo co-simulation.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.11221 [cs.AI]
(or arXiv:2608.11221v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.11221
arXiv-issued DOI via DataCite
Journal reference: ACM/IEEE 29th International Conference on Model Driven Engineering Languages and Systems, Oct 2026, Malaga, Spain
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From: Team Kairos [view email] [via CCSD proxy] [v1] Wed, 22 Jul 2026 09:24:44 UTC (3,325 KB)
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