6 papers
Robust Parameter Learning for Uncertain MDPs
Yannik Schnitzer, Alessandro Abate, David Parker
Learning-based approaches to verifying unknown Markov decision processes (MDPs) often employ uncertain MDPs. These models use, for example, confidence intervals to capture transiti…
Multi-Property Synthesis
Christoph Weinhuber, Yannik Schnitzer, Alessandro Abate +3
We study LTLf synthesis with multiple properties, where satisfying all properties may be impossible. Instead of enumerating subsets of properties, we compute in one fixed-point com…
Existence and Synthesis of Multi-Resolution Approximate Bisimulations for Continuous-State Dynamical Systems
Rudi Coppola, Yannik Schnitzer, Mirco Giacobbe +2
We present a fully automatic framework for synthesising compact, finite-state deterministic abstractions of deterministic, continuous-state autonomous systems under locally specifi…
Efficient Solution and Learning of Robust Factored MDPs
Yannik Schnitzer, Alessandro Abate, David Parker
Robust Markov decision processes (r-MDPs) extend MDPs by explicitly modelling epistemic uncertainty about transition dynamics. Learning r-MDPs from interactions with an unknown env…
Branching Bisimulation Learning
Alessandro Abate, Mirco Giacobbe, Christian Micheletti +1
We introduce a bisimulation learning algorithm for non-deterministic transition systems. We generalise bisimulation learning to systems with bounded branching and extend its applic…
Certified Approximate Reachability (CARe): Formal Error Bounds on Deep Learning of Reachable Sets
Prashant Solanki, Nikolaus Vertovec, Yannik Schnitzer +3
Recent approaches to leveraging deep learning for computing reachable sets of continuous-time dynamical systems have gained popularity over traditional level-set methods, as they o…