8 papers
An Information Theory of Finite Abstractions and their Fundamental Scalability Limits
Giannis Delimpaltadakis, Gabriel Gleizer
Finite abstractions are discrete approximations of dynamical systems, such that the set of abstraction trajectories contains all system trajectories. There is a consensus that abst…
Formal Entropy-Regularized Control of Stochastic Systems
Menno van Zutphen, Giannis Delimpaltadakis, Duarte J. Antunes
Analyzing and controlling system entropy is a powerful tool for regulating predictability of control systems. Applications benefiting from such approaches range from reinforcement…
Safe Feedback Optimization through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Feedback optimization refers to a class of methods that steer a control system to a steady state that solves an optimization problem. Despite tremendous progress on the topic, an i…
Predictable Interval MDPs through Entropy Regularization
Menno van Zutphen, Giannis Delimpaltadakis, Maurice Heemels +1
Regularization of control policies using entropy can be instrumental in adjusting predictability of real-world systems. Applications benefiting from such approaches range from, e.g…
Tempering the Bayes Filter towards Improved Model-Based Estimation
Menno van Zutphen, Domagoj Herceg, Giannis Delimpaltadakis +1
Model-based filtering is often carried out while subject to an imperfect model, as learning partially-observable stochastic systems remains a challenge. Recent work on Bayesian inf…
Feedback Optimization with State Constraints through Control Barrier Functions
Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés +1
Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that ari…