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
