Section: Research Program

Control of quantitative systems

The main objective of this research axis is to explore the quantitative and/or distributed extensions of classical control problems. We envision control in its widest meaning of driving a system in order to guarantee or enforce some extra property (i.e. not guaranteed by the system alone), in a partially- or totally-observed setting. This property can either be logical (e.g. reachability or safety) or quantitative (e.g. reach some performance level). These problems have of course an offline facet (e.g. controller design, existence of a policy/strategy) and an online facet (e.g. algorithm to select some optimal action at runtime).

Our objectives comprise classical controler synthesis for discrete-event systems, with extensions to temporal/stochastic/reward settings. They also cover maintaining or maximizing extra properties such as diagnosability or opacity, for example in stochastic systems. We also target further analysis of POMDPs (partially-observed Markov decision processes), and multi-agent versions of policy synthesis relying on tools from game theory. We aim at adressing some control problems motivated by industrial applications, that raise issues like the optimal control of timed and stochastic discrete-event systems, with concerns like robustness to perturbations and multicriteria optimization. Finally, we also plan to work on modular testing, and on runtime enforcement techniques, in order to garantee extra logical and temporal properties to event flows.