66 research outputs found
On the analysis of stochastic timed systems
The formal methods approach to develop reliable and efficient safety- or performance-critical systems is to construct mathematically precise models of such systems on which properties of interest, such as safety guarantees or performance requirements, can be verified automatically. In this thesis, we present techniques that extend the reach of exhaustive and statistical model checking to verify reachability and reward-based properties of compositional behavioural models that support quantitative aspects such as real time and randomised decisions.
We present two techniques that allow sound statistical model checking for the nondeterministic-randomised model of Markov decision processes. We investigate the relationship between two different definitions of the model of probabilistic timed automata, as well as potential ways to apply statistical model checking. Stochastic timed automata allow nondeterministic choices as well as nondeterministic and stochastic delays, and we present the first exhaustive model checking algorithm that allows their analysis. All the approaches introduced in this thesis are implemented as part of the Modest Toolset, which supports the construction and verification of models specified in the formal modelling language Modest. We conclude by applying this language and toolset to study novel distributed control strategies for photovoltaic microgenerators
Correct Probabilistic Model Checking with Floating-Point Arithmetic
Probabilistic model checking computes probabilities and expected values
related to designated behaviours of interest in Markov models. As a formal
verification approach, it is applied to critical systems; thus we trust that
probabilistic model checkers deliver correct results. To achieve scalability
and performance, however, these tools use finite-precision floating-point
numbers to represent and calculate probabilities and other values. As a
consequence, their results are affected by rounding errors that may accumulate
and interact in hard-to-predict ways. In this paper, we show how to implement
fast and correct probabilistic model checking by exploiting the ability of
current hardware to control the direction of rounding in floating-point
calculations. We outline the complications in achieving correct rounding from
higher-level programming languages, describe our implementation as part of the
Modest Toolset's 'mcsta' model checker, and exemplify the tradeoffs between
performance and correctness in an extensive experimental evaluation across
different operating systems and CPU architectures
On the analysis of stochastic timed systems
The formal methods approach to develop reliable and efficient safety- or performance-critical systems is to construct mathematically precise models of such systems on which properties of interest, such as safety guarantees or performance requirements, can be verified automatically. In this thesis, we present techniques that extend the reach of exhaustive and statistical model checking to verify reachability and reward-based properties of compositional behavioural models that support quantitative aspects such as real time and randomised decisions.
We present two techniques that allow sound statistical model checking for the nondeterministic-randomised model of Markov decision processes. We investigate the relationship between two different definitions of the model of probabilistic timed automata, as well as potential ways to apply statistical model checking. Stochastic timed automata allow nondeterministic choices as well as nondeterministic and stochastic delays, and we present the first exhaustive model checking algorithm that allows their analysis. All the approaches introduced in this thesis are implemented as part of the Modest Toolset, which supports the construction and verification of models specified in the formal modelling language Modest. We conclude by applying this language and toolset to study novel distributed control strategies for photovoltaic microgenerators.Formale Methoden erlauben die Entwicklung verlässlicher und performanter sicherheits- oder zeitkritischer Systeme, indem auf mathematisch präzisen Modellen relevante Eigenschaften wie Sicherheits- oder Performance-Garantien automatisch verifiziert werden. In dieser Dissertation stellen wir Methoden vor, mit denen die Anwendbarkeit der klassischen und statistischen Modellprüfung (model checking) zur Verifikation von Erreichbarkeits- und Nutzenseigenschaften auf kompositionellen Verhaltensmodellen, die quantitative Aspekte wie zufallsbasierte Entscheidungen und Echtzeitverhalten enthalten, erweitert wird.
Wir zeigen zwei Methoden auf, die eine korrekte statistische Modellprüfung von Markov-Entscheidungsprozessen erlauben. Wir untersuchen den Zusammenhang zwischen zwei Definitionen des Modells des probabilistischen Zeitautomaten sowie mögliche Wege, die statistische Modellprüfung auf diese Art Modelle anzuwenden. Stochastische Zeitautomaten erlauben nichtdeterministische Entscheidungen sowie nichtdeterministische und stochastische Wartezeiten; wir stellen den ersten Algorithmus für die klassische Modellprüfung dieser Automaten vor. Alle Techniken, die wir in dieser Dissertation behandeln, sind als Teil des Modest Toolsets, welches die Erstellung und Verifikation von Modellen mittels der formalen Modellierungssprache Modest erlaubt, implementiert. Wir verwenden diese Sprache und Tools, um neuartige verteilte Steuerungsalgorithmen für Photovoltaikanlagen zu untersuchen
Optimistic Value Iteration
Markov decision processes are widely used for planning and verification in
settings that combine controllable or adversarial choices with probabilistic
behaviour. The standard analysis algorithm, value iteration, only provides a
lower bound on unbounded probabilities or reward values. Two "sound"
variations, which also deliver an upper bound, have recently appeared. In this
paper, we present optimistic value iteration, a new sound approach that
leverages value iteration's ability to usually deliver tight lower bounds: we
obtain a lower bound via standard value iteration, use the result to "guess" an
upper bound, and prove the latter's correctness. Optimistic value iteration is
easy to implement, does not require extra precomputations or a priori state
space transformations, and works for computing reachability probabilities as
well as expected rewards. It is also fast, as we show via an extensive
experimental evaluation using our publicly available implementation within the
Modest Toolset
Symblicit Exploration and Elimination for Probabilistic Model Checking
Binary decision diagrams can compactly represent vast sets of states,
mitigating the state space explosion problem in model checking. Probabilistic
systems, however, require multi-terminal diagrams storing rational numbers.
They are inefficient for models with many distinct probabilities and for
iterative numeric algorithms like value iteration. In this paper, we present a
new "symblicit" approach to checking Markov chains and related probabilistic
models: We first generate a decision diagram that symbolically collects all
reachable states and their predecessors. We then concretise states one-by-one
into an explicit partial state space representation. Whenever all predecessors
of a state have been concretised, we eliminate it from the explicit state space
in a way that preserves all relevant probabilities and rewards. We thus keep
few explicit states in memory at any time. Experiments show that very large
models can be model-checked in this way with very low memory consumption
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
- …