32 citations · 70 across the 23 of their papers we have counts for
23 papers
Robust Shielding for Safe Reinforcement Learning
Edwin Hamel-De le Court, Thom Badings, Alessandro Abate +2
Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs). However, existing shielding techniques typ…
Temporal Logic Control of Nonlinear Stochastic Systems with Online Performance Optimization
Alessandro Riccardi, Thom Badings, Luca Laurenti +2
The deployment of autonomous systems in safety-critical environments requires control policies that guarantee satisfaction of complex control specifications. These systems are comm…
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings +2
Control barrier functions (CBFs) are a popular tool for safety certification of nonlinear dynamical control systems. Recently, CBFs represented as neural networks have shown great…
Data-Driven Abstraction and Synthesis for Stochastic Systems with Unknown Dynamics
Mahdi Nazeri, Thom Badings, Anne-Kathrin Schmuck +2
We study the automated abstraction-based synthesis of correct-by-construction control policies for stochastic dynamical systems with unknown dynamics. Our approach is to learn an a…
Probabilistic Alternating Simulations for Policy Synthesis in Uncertain Stochastic Dynamical Systems
Thom Badings, Alessandro Abate
A classical approach to formal policy synthesis in stochastic dynamical systems is to construct a finite-state abstraction, often represented as a Markov decision process (MDP). Th…
Best-Effort Policies for Robust Markov Decision Processes
Alessandro Abate, Thom Badings, Giuseppe De Giacomo +1
We study the common generalization of Markov decision processes (MDPs) with sets of transition probabilities, known as robust MDPs (RMDPs). A standard goal in RMDPs is to compute a…
Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems
Francisco Souza, Thom Badings, Geert Postma +1
New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability an…
Data-Driven Yet Formal Policy Synthesis for Stochastic Nonlinear Dynamical Systems
Mahdi Nazeri, Thom Badings, Sadegh Soudjani +1
The automated synthesis of control policies for stochastic dynamical systems presents significant challenges. A standard approach is to construct a finite-state abstraction of the…
Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet
Marnix Suilen, Thom Badings, Eline M. Bovy +2
Markov decision processes (MDPs) are a standard model for sequential decision-making problems and are widely used across many scientific areas, including formal methods and artific…
Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates
Thom Badings, Wietze Koops, Sebastian Junges +1
We consider the verification of neural network policies for discrete-time stochastic systems with respect to reach-avoid specifications. We use a learner-verifier procedure that le…
A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions
Thom Badings, Licio Romao, Alessandro Abate +1
Finite-state abstractions are widely studied for the automated synthesis of correct-by-construction controllers for stochastic dynamical systems. However, existing abstraction meth…
CTMCs with Imprecisely Timed Observations
Thom Badings, Matthias Volk, Sebastian Junges +2
Labeled continuous-time Markov chains (CTMCs) describe processes subject to random timing and partial observability. In applications such as runtime monitoring, we must incorporate…
Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions
Thom Badings, Nils Jansen, Licio Romao +1
Automated synthesis of correct-by-construction controllers for autonomous systems is crucial for their deployment in safety-critical scenarios. Such autonomous systems are naturall…
Efficient Sensitivity Analysis for Parametric Robust Markov Chains
Thom Badings, Sebastian Junges, Ahmadreza Marandi +2
We provide a novel method for sensitivity analysis of parametric robust Markov chains. These models incorporate parameters and sets of probability distributions to alleviate the of…
Decision-Making Under Uncertainty: Beyond Probabilities
Thom Badings, Thiago D. Simão, Marnix Suilen +1
This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in…
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions
Thom Badings, Licio Romao, Alessandro Abate +4
Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynam…
Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics
Luke Rickard, Thom Badings, Licio Romao +1
Automated synthesis of provably correct controllers for cyber-physical systems is crucial for deployment in safety-critical scenarios. However, hybrid features and stochastic or un…
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty
Thom Badings, Licio Romao, Alessandro Abate +1
Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of…
Sampling-Based Verification of CTMCs with Uncertain Rates
Thom S. Badings, Nils Jansen, Sebastian Junges +2
We employ uncertain parametric CTMCs with parametric transition rates and a prior on the parameter values. The prior encodes uncertainty about the actual transition rates, while th…
Scenario-Based Verification of Uncertain Parametric MDPs
Thom Badings, Murat Cubuktepe, Nils Jansen +3
We consider parametric Markov decision processes (pMDPs) that are augmented with unknown probability distributions over parameter values. The problem is to compute the probability…
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise
Thom S. Badings, Alessandro Abate, Nils Jansen +3
Controllers for autonomous systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modelled as process noise, and com…
Correct-by-construction reach-avoid control of partially observable linear stochastic systems
Thom Badings, Hasan A. Poonawala, Marielle Stoelinga +1
We study feedback controller synthesis for reach-avoid control of discrete-time, linear time-invariant (LTI) systems with Gaussian process and measurement noise. The problem is to…
Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids
Thom S. Badings, Arnd Hartmanns, Nils Jansen +1
We study a smart grid with wind power and battery storage. Traditionally, day-ahead planning aims to balance demand and wind power, yet actual wind conditions often deviate from fo…