The Systems Modeling and Optimization Laboratory establishes method before application. In systems modelling it expresses the system behind an optimisation problem in mathematical language and derives its solution; in intelligent optimisation it studies solutions in which a computer reads its environment, learns, and optimises on its own. Reinforcement learning, simulation-based optimisation and stochastic control are the fields it works in, all of them directly connected to the theoretical foundations of AI today.