The research area of Intelligent Simulation within LIACC is devoted to devising simulation theories and methodologies highly enriched with AI, in all phases of the simulation project lifecycle. From a more practical perspective, other research areas and thematic lines profit from intelligent simulation as the natural grounds for empirical AI, allowing intelligent systems to be tested with and evaluated appropriately. Another important use of simulation is for decision support, resorting to different modelling purposes, namely descriptive, normative, predictive, speculative (also called as scenarisation, allowing for what-if analysis), and prescriptive.
LIACC likewise explores advanced methods of modelling and simulation, including distributed simulation techniques and simulator interoperability, human-in-the-loop and participatory simulation, synthetisation of artificial systems and digital twins, simulation games, and virtual reality for immersive behavioral simulation. Additionally to different modelling techniques and discrete systems modelling metaphors, the Intelligent Simulation research area gives special emphasis to agent-based modelling and simulation. On the one hand, the agent and multiagent system metaphors are particularly suitable to support the analysis of highly complex and dynamic systems.
On the other hand, agent-driven simulation offers a plethora of opportunities for the development of intelligent simulation methods and tools, supporting the optimisation of parametric and meta-models and improving the efficiency of large-scale, multi-parametric, and multi-objective simulation models. Application-wise, LIACC has applied Intelligent Simulation in different domains, including intelligent robotics, social simulation and complex systems analysis, autonomous vehicles and multi-vehicle mission coordination, intelligent transportation and mobility systems, logistics, disruption management, sports, digital games, behavioural modelling, data synthetisation, and many others.