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

Lightweight Statistical Model Checking in Nondeterministic Continuous Time

Lightweight scheduler sampling brings statistical model checking to nondeterministic formalisms with undiscounted properties, in constant memory. Its direct application to continuous-time models is rendered ineffective by their dense concrete state spaces and the need to consider continuous input for optimal decisions. In this paper we describe the challenges and state of the art in applying lightweight scheduler sampling to three continuous-time formalisms: After a review of recent work on exploiting discrete abstractions for probabilistic timed automata, we discuss scheduler sampling for Markov automata and apply it on two case studies. We provide further insights into the tradeoffs between scheduler classes for stochastic automata. Throughout, we present extended experiments and new visualisations of the distribution of schedulers.</p

Policy learning for time-bounded reachability in Continuous-Time Markov Decision Processes via doubly-stochastic gradient ascent

Continuous-time Markov decision processes are an important class of models in a wide range of applications, ranging from cyber-physical systems to synthetic biology. A central problem is how to devise a policy to control the system in order to maximise the probability of satisfying a set of temporal logic specifications. Here we present a novel approach based on statistical model checking and an unbiased estimation of a functional gradient in the space of possible policies. The statistical approach has several advantages over conventional approaches based on uniformisation, as it can also be applied when the model is replaced by a black box, and does not suffer from state-space explosion. The use of a stochastic gradient to guide our search considerably improves the efficiency of learning policies. We demonstrate the method on a proof-of-principle non-linear population model, showing strong performance in a non-trivial task

A Modest Approach to Modelling and Checking Markov Automata (Artifact)

Markov automata are a compositional modelling formalism with continuous stochastic time, discrete probabilities, and nondeterministic choices. In our QEST 2019 paper titled "A Modest Approach to Modelling and Checking Markov Automata", we present extensions to the Modest language and the 'mcsta' model checker of the Modest Toolset to describe and analyse Markov automata models. The verification of Markov automata models requires dedicated algorithms for time-bounded probabilistic reachability and long-run average rewards. In the paper, we describe several recently developed such algorithms as implemented in 'mcsta' and evaluate them on a comprehensive set of benchmarks. Our evaluation shows that 'mcsta' improves the performance and scalability of Markov automata model checking compared to earlier and alternative tools. This artifact contains (1) the version of 'mcsta' and (2) the model files used for our experiments, (3) the raw experimental results, and (4) Linux scripts to replicate the experiments

Evaluation of the content and composition of volatile petroleum hydrocarbons in soil by solid-phase microextraction and gas chromatography/mass spectrometry

Изучена возможность применения метода твердофазной микроэкстракции в сочетании с ГХ/МС для характеристики летучих нефтяных углеводородов в почве и донных отложениях. Рассмотрены диагностические признаки летучих углеводородов, выделяющихся в газовую фазу и показана возможность идентификации и количественной оценки, как общего содержания летучих продуктов, так и отдельных групп углеводородов и индивидуальных соединений в почвах и донных отложениях с использованием внутренних стандартов и градуировки по н-алканам. Этим методом охарактеризованы летучие компоненты нефтепродуктов в донных отложениях пруда-отстойника на территории промышленного предприятия.The feasibility of using solid-phase microextraction and gas chromatography/mass spectrometry for the identification and quantification of volatile petroleum hydrocarbons in soils and sediments was studied. Suitable diagnostic features provided identification of hydrocarbons released to gas phase. Quantitation both the total content of volatile hydrocarbons and hydrocarbon types/individual compounds was performed using internal standards and calibration with n-alkanes. Volatile components in the sediments of the sedimentation pond in industrial plant were characterized as well as their migration and weathering

The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models - (QComp 2019 Competition Report)

A great publication