| Popis: |
The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness. |
