2 citations · 3 across the 8 of their papers we have counts for
10 papers
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…
Optimization-Based Robust Permissive Synthesis for Interval MDPs
Khang Vo Huynh, David Parker, Lu Feng
We present an optimization-based framework for robust permissive synthesis for Interval Markov Decision Processes (IMDPs). While robust IMDP controller synthesis typically yields a…
Robust Probabilistic Bisimilarity for Labelled Markov Chains
Syyeda Zainab Fatmi, Stefan Kiefer, David Parker +1
Despite its prevalence, probabilistic bisimilarity suffers from a lack of robustness under minuscule perturbations of the transition probabilities. This can lead to discontinuities…
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…
Learning Algorithms for Verification of Markov Decision Processes
Tomáš Brázdil, Krishnendu Chatterjee, Martin Chmelik +6
We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques…