1,038 research outputs found
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
Automated Experiment Design for Data-Efficient Verification of Parametric Markov Decision Processes
We present a new method for statistical verification of quantitative
properties over a partially unknown system with actions, utilising a
parameterised model (in this work, a parametric Markov decision process) and
data collected from experiments performed on the underlying system. We obtain
the confidence that the underlying system satisfies a given property, and show
that the method uses data efficiently and thus is robust to the amount of data
available. These characteristics are achieved by firstly exploiting parameter
synthesis to establish a feasible set of parameters for which the underlying
system will satisfy the property; secondly, by actively synthesising
experiments to increase amount of information in the collected data that is
relevant to the property; and finally propagating this information over the
model parameters, obtaining a confidence that reflects our belief whether or
not the system parameters lie in the feasible set, thereby solving the
verification problem.Comment: QEST 2017, 18 pages, 7 figure
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
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
Stochastic model checking for predicting component failures and service availability
When a component fails in a critical communications service, how urgent is a repair? If we repair within 1 hour, 2 hours, or
n hours, how does this affect the likelihood of service failure? Can a formal model support assessing the impact, prioritisation, and
scheduling of repairs in the event of component failures, and forecasting of maintenance costs? These are some of the questions
posed to us by a large organisation and here we report on our experience of developing a stochastic framework based on a discrete
space model and temporal logic to answer them. We define and explore both standard steady-state and transient temporal logic
properties concerning the likelihood of service failure within certain time bounds, forecasting maintenance costs, and we introduce a
new concept of envelopes of behaviour that quantify the effect of the status of lower level components on service availability. The
resulting model is highly parameterised and user interaction for experimentation is supported by a lightweight, web-based interface
A Statistical Model Checker for Nondeterminism and Rare Events
A great publication
Multiple verification in computational modeling of bone pathologies
We introduce a model checking approach to diagnose the emerging of bone
pathologies. The implementation of a new model of bone remodeling in PRISM has
led to an interesting characterization of osteoporosis as a defective bone
remodeling dynamics with respect to other bone pathologies. Our approach allows
to derive three types of model checking-based diagnostic estimators. The first
diagnostic measure focuses on the level of bone mineral density, which is
currently used in medical practice. In addition, we have introduced a novel
diagnostic estimator which uses the full patient clinical record, here
simulated using the modeling framework. This estimator detects rapid (months)
negative changes in bone mineral density. Independently of the actual bone
mineral density, when the decrease occurs rapidly it is important to alarm the
patient and monitor him/her more closely to detect insurgence of other bone
co-morbidities. A third estimator takes into account the variance of the bone
density, which could address the investigation of metabolic syndromes, diabetes
and cancer. Our implementation could make use of different logical combinations
of these statistical estimators and could incorporate other biomarkers for
other systemic co-morbidities (for example diabetes and thalassemia). We are
delighted to report that the combination of stochastic modeling with formal
methods motivate new diagnostic framework for complex pathologies. In
particular our approach takes into consideration important properties of
biosystems such as multiscale and self-adaptiveness. The multi-diagnosis could
be further expanded, inching towards the complexity of human diseases. Finally,
we briefly introduce self-adaptiveness in formal methods which is a key
property in the regulative mechanisms of biological systems and well known in
other mathematical and engineering areas.Comment: In Proceedings CompMod 2011, arXiv:1109.104
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