Research team

Constrained Systems Lab (CoSys-Lab)

Expertise

Software Engineering; Model-driven Engineering; Model-based System Engineering; Domain-specific Languages; Multi-agent Systems; Intelligent Agents;

Intelligent Software Agents and Multi-agent Systems for the Lifecycle of Smart Cyber-physical System-of-Systems. 01/10/2020 - 30/09/2024

Abstract

AnSyMo (Antwerp Systems and Software Modeling) is a Computer Science research group investigating foundations, techniques, methods and tools for the design, analysis and maintenance of software-intensive systems. MICSS (Modeling Intelligente Complex Software and Systems) is a lab in AnSyMo group dedicated to the modeling of intelligent systems such as smart cyber-physical system of systems using intelligent agents and model driven engineering techniques. Cyber-Physical Systems (CPS) consist of tightly integrated and coordinated computational and physical elements. They are the evolution of embedded systems to a higher level of complexity, focusing on the interaction with highly uncertain physical environments (such as human interaction or wear & tear of devices). In these systems, embedded computers and networks monitor (through sensors) and control (through actuators) the physical processes, usually with feedback loops where physical processes and computations affect each other. The computational part of these systems plays a key role and needs to be developed in a way that can handle uncertain situations with the limited resources (including computational resource, memory resource, communication resource, and so on), mostly in real-time. With IoT and Industry 4.0 maturing, these systems are getting interconnected and making a complex larger system called the Cyber-physical System of Systems (CPSoS) to serve in more sophisticated tasks. In these systems, CPSs are working as part of a large system that is spatially distributed, has no central control, has autonomous subsystems, is dynamically configured, has emergent behaviour, and is continually evolving, even at runtime. A key point in CPSoS is to obtain knowledge out of the information that is collected by distributed monitoring of the environment, using artificial intelligence techniques. This knowledge can improve the control and feedback mechanism. Further, these capabilities lead to the smart systems of the future with timely and more accurate decisions and actions, called Smart CPSoS (sCPSoS), which can help to address a number of social, industrial, and environmental issues. This project aims to address the challenges of sCPSoS using intelligent agents and model-driven engineering techniques.

Researcher(s)

Research team(s)

Modelling and Simulation of Cyber-Physical Systems 01/08/2020 - 31/07/2025

Abstract

Cyber-Physical Systems (CPS) consist of tightly integrated and coordinated computational and physical elements. They focus on interaction with highly uncertain environment with the limited resources. By introducing IoT and Industry 4.0, the CPSs are connected to each other to meet the emerging more complex requirements. These interconnected CPSs constitute complex systems called Cyber-physical System of Systems (CPSoS) in which there may be emergent behaviour, lack of central control, dynamic structure, and need for autonomy. Therefore, CPSoS cannot be designed and managed using theories and tools from only one single domain. A key point in a CPSoS is to obtain knowledge out of the information, collected by monitoring the environment. This knowledge can improve the control and feedback mechanism. This capability leads to the next generation of CPSoS with timely and more accurate decisions and actions called Smart CPSoS (sCPSoS). These smart systems can analyze a situation and make decisions based on the available data in an adaptive manner, to perform smart actions. However, such intelligent techniques put yet additional complexity to the systems, specifically to the computational part. Thus, these systems of the future have a high complexity (both from structural and behavioural points of view) throughout their lifecycle, including modeling & simulation, design & implementation, validation & verification, deployment, execution & monitoring, and maintenance & evolution. There is a need for new methodologies, architectures, process models, and frameworks to tackle this complexity. To overcome the challenges in the development and operation of sCPSoS, modeling techniques can be used for different aspect and various levels of abstraction in the system. To this end, appropriate modeling paradigms should be chosen for each aspect/level. These models and modeling paradigms will be integrated, hence called multi-paradigm modeling (MPM), to represent the whole system. Specifically, the idea is to integrate agent paradigm with the model-based system engineering (MBSE) for both modelling & simulation phase as well as execution and monitoring phase in the lifecycle of sCPSoS. Agent based system engineering (ABSE) uses software intelligent agents to successfully cross-fertilize the fields of systems engineering and artificial intelligence. In this way, the autonomy, dynamic behaviour and smart-ness of sCPSoS can be handled by intelligent agents, integrated with MBSE models.

Researcher(s)

Research team(s)