480 research outputs found
A Hierarchy of Scheduler Classes for Stochastic Automata
Stochastic automata are a formal compositional model for concurrent
stochastic timed systems, with general distributions and non-deterministic
choices. Measures of interest are defined over schedulers that resolve the
nondeterminism. In this paper we investigate the power of various theoretically
and practically motivated classes of schedulers, considering the classic
complete-information view and a restriction to non-prophetic schedulers. We
prove a hierarchy of scheduler classes w.r.t. unbounded probabilistic
reachability. We find that, unlike Markovian formalisms, stochastic automata
distinguish most classes even in this basic setting. Verification and strategy
synthesis methods thus face a tradeoff between powerful and efficient classes.
Using lightweight scheduler sampling, we explore this tradeoff and demonstrate
the concept of a useful approximative verification technique for stochastic
automata
Smart Sampling for Lightweight Verification of Markov Decision Processes
Markov decision processes (MDP) are useful to model optimisation problems in
concurrent systems. To verify MDPs with efficient Monte Carlo techniques
requires that their nondeterminism be resolved by a scheduler. Recent work has
introduced the elements of lightweight techniques to sample directly from
scheduler space, but finding optimal schedulers by simple sampling may be
inefficient. Here we describe "smart" sampling algorithms that can make
substantial improvements in performance.Comment: IEEE conference style, 11 pages, 5 algorithms, 11 figures, 1 tabl
Scalable Verification of Markov Decision Processes
Markov decision processes (MDP) are useful to model concurrent process
optimisation problems, but verifying them with numerical methods is often
intractable. Existing approximative approaches do not scale well and are
limited to memoryless schedulers. Here we present the basis of scalable
verification for MDPSs, using an O(1) memory representation of
history-dependent schedulers. We thus facilitate scalable learning techniques
and the use of massively parallel verification.Comment: V4: FMDS version, 12 pages, 4 figure
A Statistical Model Checker for Nondeterminism and Rare Events
A great publication
A modest approach to Markov automata
A duplicate of https://zenodo.org/record/5758839.
Reason: The submitter forgot to indicate the DOI before publishing, so it got another one assigned automatically, which is unchangeable
An Overview of Modest Models and Tools for Real Stochastic Timed Systems
We depend on the safe, reliable, and timely operation of cyber-physical
systems ranging from smart grids to avionics components. Many of them involve
time-dependent behaviours and are subject to randomness. Modelling languages
and verification tools thus need to support these quantitative aspects. In my
invited presentation at MARS 2022, I gave an introduction to quantitative
verification using the Modest modelling language and the Modest Toolset, and
highlighted three recent case studies with increasing demands on model
expressiveness and tool capabilities: A case of power supply noise in a
network-on-chip modelled as a Markov chain; a case of message routing in
satellite constellations that uses Markov decision processes with distributed
information; and a case of optimising an attack on Bitcoin via Markov automata
model checking. This paper summarises the presentation.Comment: In Proceedings MARS 2022, arXiv:2203.0929
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