4,908 research outputs found
Probabilistic Bisimulations for PCTL Model Checking of Interval MDPs
Verification of PCTL properties of MDPs with convex uncertainties has been
investigated recently by Puggelli et al. However, model checking algorithms
typically suffer from state space explosion. In this paper, we address
probabilistic bisimulation to reduce the size of such an MDPs while preserving
PCTL properties it satisfies. We discuss different interpretations of
uncertainty in the models which are studied in the literature and that result
in two different definitions of bisimulations. We give algorithms to compute
the quotients of these bisimulations in time polynomial in the size of the
model and exponential in the uncertain branching. Finally, we show by a case
study that large models in practice can have small branching and that a
substantial state space reduction can be achieved by our approach.Comment: In Proceedings SynCoP 2014, arXiv:1403.784
Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes
Interval Markov decision processes (IMDPs) generalise classical MDPs by
having interval-valued transition probabilities. They provide a powerful
modelling tool for probabilistic systems with an additional variation or
uncertainty that prevents the knowledge of the exact transition probabilities.
In this paper, we consider the problem of multi-objective robust strategy
synthesis for interval MDPs, where the aim is to find a robust strategy that
guarantees the satisfaction of multiple properties at the same time in face of
the transition probability uncertainty. We first show that this problem is
PSPACE-hard. Then, we provide a value iteration-based decision algorithm to
approximate the Pareto set of achievable points. We finally demonstrate the
practical effectiveness of our proposed approaches by applying them on several
case studies using a prototypical tool.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach
Goals are first-class entities in a self-adaptive system (SAS) as they guide
the self-adaptation. A SAS often operates in dynamic and partially unknown
environments, which cause uncertainty that the SAS has to address to achieve
its goals. Moreover, besides the environment, other classes of uncertainty have
been identified. However, these various classes and their sources are not
systematically addressed by current approaches throughout the life cycle of the
SAS. In general, uncertainty typically makes the assurance provision of SAS
goals exclusively at design time not viable. This calls for an assurance
process that spans the whole life cycle of the SAS. In this work, we propose a
goal-oriented assurance process that supports taming different sources (within
different classes) of uncertainty from defining the goals at design time to
performing self-adaptation at runtime. Based on a goal model augmented with
uncertainty annotations, we automatically generate parametric symbolic formulae
with parameterized uncertainties at design time using symbolic model checking.
These formulae and the goal model guide the synthesis of adaptation policies by
engineers. At runtime, the generated formulae are evaluated to resolve the
uncertainty and to steer the self-adaptation using the policies. In this paper,
we focus on reliability and cost properties, for which we evaluate our approach
on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the
validation are promising and show that our approach is able to systematically
tame multiple classes of uncertainty, and that it is effective and efficient in
providing assurances for the goals of self-adaptive systems
Qualitative Reachability for Open Interval Markov Chains
Interval Markov chains extend classical Markov chains with the possibility to
describe transition probabilities using intervals, rather than exact values.
While the standard formulation of interval Markov chains features closed
intervals, previous work has considered also open interval Markov chains, in
which the intervals can also be open or half-open. In this paper we focus on
qualitative reachability problems for open interval Markov chains, which
consider whether the optimal (maximum or minimum) probability with which a
certain set of states can be reached is equal to 0 or 1. We present
polynomial-time algorithms for these problems for both of the standard
semantics of interval Markov chains. Our methods do not rely on the closure of
open intervals, in contrast to previous approaches for open interval Markov
chains, and can characterise situations in which probability 0 or 1 can be
attained not exactly but arbitrarily closely.Comment: Full version of a paper published at RP 201
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
CIGALEMC: Galaxy Parameter Estimation using a Markov Chain Monte Carlo Approach with Cigale
We introduce a fast Markov Chain Monte Carlo (MCMC) exploration of the
astrophysical parameter space using a modified version of the publicly
available code CIGALE (Code Investigating GALaxy emission). The original CIGALE
builds a grid of theoretical Spectral Energy Distribution (SED) models and fits
to photometric fluxes from Ultraviolet (UV) to Infrared (IR) to put contraints
on parameters related to both formation and evolution of galaxies. Such a
grid-based method can lead to a long and challenging parameter extraction since
the computation time increases exponentially with the number of parameters
considered and results can be dependent on the density of sampling points,
which must be chosen in advance for each parameter. Markov Chain Monte Carlo
methods, on the other hand, scale approximately linearly with the number of
parameters, allowing a faster and more accurate exploration of the parameter
space by using a smaller number of efficiently chosen samples. We test our MCMC
version of the code CIGALE (called CIGALEMC) with simulated data. After
checking the ability of the code to retrieve the input parameters used to build
the mock sample, we fit theoretical SEDs to real data from the well known and
studied SINGS sample. We discuss constraints on the parameters and show the
advantages of our MCMC sampling method in terms of accuracy of the results and
optimization of CPU time.Comment: 12 pages, 8 figures, 4 tables, updated to match the version accepted
for publication in ApJ; code available at http://www.oamp.fr/cigale
Recovering stellar population parameters via two full-spectrum fitting algorithms in the absence of model uncertainties
Using mock spectra based on Vazdekis/MILES library fitted within the
wavelength region 3600-7350\AA, we analyze the bias and scatter on the
resulting physical parameters induced by the choice of fitting algorithms and
observational uncertainties, but avoid effects of those model uncertainties. We
consider two full-spectrum fitting codes: pPXF and STARLIGHT, in fitting for
stellar population age, metallicity, mass-to-light ratio, and dust extinction.
With pPXF we find that both the bias in the population parameters and the
scatter in the recovered logarithmic values follows the expected trend. The
bias increases for younger ages and systematically makes recovered ages older,
larger and metallicities lower than the true values. For reference,
at S/N=30, and for the worst case (yr), the bias is 0.06 dex in
, 0.03 dex in both age and [M/H]. There is no significant dependence
on either E(B-V) or the shape of the error spectrum. Moreover, the results are
consistent for both our 1-SSP and 2-SSP tests. With the STARLIGHT algorithm, we
find trends similar to pPXF, when the input E(B-V)<0.2 mag. However, with
larger input E(B-V), the biases of the output parameter do not converge to zero
even at the highest S/N and are strongly affected by the shape of the error
spectra. This effect is particularly dramatic for youngest age, for which all
population parameters can be strongly different from the input values, with
significantly underestimated dust extinction and [M/H], and larger ages and
. Results degrade when moving from our 1-SSP to the 2-SSP tests. The
STARLIGHT convergence to the true values can be improved by increasing Markov
Chains and annealing loops to the "slow mode". For the same input spectrum,
pPXF is about two order of magnitudes faster than STARLIGHT's "default mode"
and about three order of magnitude faster than STARLIGHT's "slow mode".Comment: Accepted for publication in MNRAS. 17 pages, 17 figure
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