Supporting Self-Management in Cyber-Physical Systems by Combining Data-driven and Knowledge-enabled Methods

Cyber-Physical Systems (CPS) refer to intelligent systems that combine computational and physical capabilities to enable advanced functionalities, such as autonomous behaviors, human-machine interaction, and machine collaboration in complex environments. Addressing these functionalities often necessitates the adoption of Artificial Intelligence (AI) techniques which are extensively utilized for operational perception and decision-making. However, due to the inherently data-intensive and opaque nature of most AI-enabled components, combined with unforeseen environments, the integration of AI-enabled techniques into CPS presents significant engineering challenges in quality management. Self-management, as an embedded system feature, extends conventional CPS with capabilities for operation monitoring, planning and adaptation. It is often considered as a necessary mechanism for ensuring the quality and trustworthiness of AI-enabled components. However, implementing self-management in AI-enabled CPS presents several challenges: The concept of self-management varies depending on the audience and application, making its definition and implementation more complex. Additionally, the data-intensive nature of AI-enabled components requires extra effort to ensure consistent performance across operational domains, especially under unforeseen conditions. To cope with these challenges, this thesis proposes to integrate data-driven and knowledge-enabled methods for self-management through the following efforts: 1) Proposing conceptual frameworks that define the necessary and sufficient functionalities for self-management, with the support for situation awareness regarding the internal and environmental conditions; 2) Developing condition monitoring modules within the proposed conceptual frameworks for the situation-awareness to analyze system status; 3) Creating human-explainable data-driven methods for understanding operational conditions; 4) Designinglearning-based agents to dynamically and effectively address vulnerabilities inAI-enabled systems that could lead to system failures or compromise the operational safety. This thesis consolidates key concepts and introduces novel features for self-management in CPS by synthesizing insights from existing research and addressing their limitations. It provides a framework for designing learning based agents that leverage data synthesis to achieve self-management. Additionally, the thesis develops data-driven methods integrated with knowledge enabled models to enhance situation awareness and trustworthiness, effectively addressing the complexity and opacity of AI-enabled computing processes.