Modeling a Knowledge-Based System for Cyber-physical Systems: Applications in the Context of Learning Analytics

Abstract

Knowledge-based systems are major concerns in the field of artificial intelligence for the development of cyber-physical systems capable of self-management and adaptation to their context. The representation and knowledge management of these cyber-physical systems integrating heterogeneous actors must ensure the empowerment and optimization of these systems, as well as their ability to adapt to dynamic and unpredictable changes in their environment. In this document we show how a knowledge-based system based on semantic web technologies and IBM’s reference model of Autonomic Computing (AC) can offer intelligent collaboration and coordination between people, data, services, robots and connected objects in the implementation of self-management processes in cyber-physical systems. Our solution consists to design a knowledge base in the field of Learning Analytics (LAs) involving a complex range of knowledge and heterogeneous components. This ontological knowledge base is guided by a functional decomposition approach based on the operating principle of the MAPE-K (Monitor-Analyze-Plan-Execute and Knowledge) autonomous control loop to provide the system with self-management capabilities.