2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
Perturbation equivalence in labelled Markov chains
Syyeda Zainab Fatmi, Stefan Kiefer, James C. A. Main +1
Behavioural equivalences, such as language equivalence and probabilistic bisimilarity, are fundamental techniques for reducing the size of probabilistic models. However, these equi…
Robust PAC Learning of Concurrent Stochastic Games
Angel Y. He, David Parker
We introduce the first Probably Approximately Correct (PAC) learning framework for general-sum concurrent stochastic games (CSGs) with transition uncertainty, while addressing the…
Property-driven Causal Abstractions for Markov Decision Processes
Jule Schmidt, Maximilian Weininger, Clemens Dubslaff +2
Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations. The exponent…
On the Continuity of the Probabilistic Bisimilarity Distance
Syyeda Zainab Fatmi, Stefan Kiefer, David Parker +1
The probabilistic bisimilarity distance provides a quantitative measure of behavioural difference for labelled Markov chains, but it may be discontinuous under perturbations of the…
UMB: A Unified Markov Binary Format for Probabilistic Model Checking (extended version)
Roman Andriushchenko, Arnd Hartmanns, Joshua Jeppson +5
This paper presents the unified Markov binary (UMB) format, an efficient, extensible, and well-supported explicit-state file format for representing a wide range of probabilistic s…
Robust Verification of Concurrent Stochastic Games
Angel Y. He, David Parker
Autonomous systems often operate in multi-agent settings and need to make concurrent, strategic decisions, typically in uncertain environments. Verification and control problems fo…