Complex and Autonomous Systems Simulation

Distributed Simulation of Multi-Agent Systems

Complex autonomous systems involve dynamic and unpredictable interactions between large numbers of components including software, hardware devices (such as sensors), and social entities (people or collective bodies). Examples of such systems range from embedded systems such as robots and autonomous vehicles, to systems controlling critical infrastructures, such as defence and smart city systems, to biological systems, to business applications with decision-making capabilities, to social systems and services, such as e-government and e-learning, to metaverses and virtual environments

The complexity of such systems renders simulation modelling the only viable method to study their properties and analyse their emergent behaviour. Multi-agent systems (MAS) have emerged as a particularly suitable paradigm for modelling complex systems. When embedded in a real (e.g. in autonomous vehicles) or virtual system (e.g. a metaverse), a MAS is itself a complex system whose properties and emergent behaviour have also to be analysed via simulation.

The application of agent-based simulation to ever more complex problems has placed it in the highly computation intensive world with computational requirements far exceeding the memory and performance capabilities of conventional computer systems. Distributed simulation has emerged as the  only  viable approach to alleviate the simulation bottleneck in the design and analysis of large, complex, agent-based systems and meet the performance and interoperability requirements of MAS models.

With my team we have pioneered the field of Distributed Simulation of complex MAS models and have delivered a set of novel distributed simulation systems for this domain. 

Systems and Publications