2,081 research outputs found
Probabilistic Opacity for Markov Decision Processes
Opacity is a generic security property, that has been defined on (non
probabilistic) transition systems and later on Markov chains with labels. For a
secret predicate, given as a subset of runs, and a function describing the view
of an external observer, the value of interest for opacity is a measure of the
set of runs disclosing the secret. We extend this definition to the richer
framework of Markov decision processes, where non deterministic choice is
combined with probabilistic transitions, and we study related decidability
problems with partial or complete observation hypotheses for the schedulers. We
prove that all questions are decidable with complete observation and
-regular secrets. With partial observation, we prove that all
quantitative questions are undecidable but the question whether a system is
almost surely non opaque becomes decidable for a restricted class of
-regular secrets, as well as for all -regular secrets under
finite-memory schedulers
State Estimation for the Individual and the Population in Mean Field Control with Application to Demand Dispatch
This paper concerns state estimation problems in a mean field control
setting. In a finite population model, the goal is to estimate the joint
distribution of the population state and the state of a typical individual. The
observation equations are a noisy measurement of the population.
The general results are applied to demand dispatch for regulation of the
power grid, based on randomized local control algorithms. In prior work by the
authors it has been shown that local control can be carefully designed so that
the aggregate of loads behaves as a controllable resource with accuracy
matching or exceeding traditional sources of frequency regulation. The
operational cost is nearly zero in many cases.
The information exchange between grid and load is minimal, but it is assumed
in the overall control architecture that the aggregate power consumption of
loads is available to the grid operator. It is shown that the Kalman filter can
be constructed to reduce these communication requirements,Comment: To appear, IEEE Trans. Auto. Control. Preliminary version appeared in
the 54rd IEEE Conference on Decision and Control, 201
Identification of cellular automata: theoretical remarks
Land use evolution during forty years in a large set of European cities is analysed by means of a cellular automaton. In one hand (the operational level), the use of this modelling tool allows: a: to study the transition rules in land use and the proximity effects on these rules; b: to compare the different case -studies, otherwise very difficult to be confronted; c: to define scenarios of evolution, on the bases of the past trends. On the other hand (methodological level), availability of a large data-base (significant time series for a set of comparable cases) allows: a: to manage, in a scientific way, the problem of calibration and validation of a cellular automaton (a crucial problem - we have to blame - usually neglected in territorial applications); b: to verify, empirically, potentialities and limits of cellular automata, compared to other models for the analysis of spatial dynamics.
Verification and Control of Partially Observable Probabilistic Real-Time Systems
We propose automated techniques for the verification and control of
probabilistic real-time systems that are only partially observable. To formally
model such systems, we define an extension of probabilistic timed automata in
which local states are partially visible to an observer or controller. We give
a probabilistic temporal logic that can express a range of quantitative
properties of these models, relating to the probability of an event's
occurrence or the expected value of a reward measure. We then propose
techniques to either verify that such a property holds or to synthesise a
controller for the model which makes it true. Our approach is based on an
integer discretisation of the model's dense-time behaviour and a grid-based
abstraction of the uncountable belief space induced by partial observability.
The latter is necessarily approximate since the underlying problem is
undecidable, however we show how both lower and upper bounds on numerical
results can be generated. We illustrate the effectiveness of the approach by
implementing it in the PRISM model checker and applying it to several case
studies, from the domains of computer security and task scheduling
Diagnosis in Infinite-State Probabilistic Systems
In a recent work, we introduced four variants of diagnosability
(FA, IA, FF, IF) in (finite) probabilistic
systems (pLTS) depending whether one considers (1) finite or
infinite runs and (2) faulty or all runs. We studied their
relationship and established that the corresponding decision
problems are PSPACE-complete. A key ingredient of the decision
procedures was a characterisation of diagnosability by the fact that
a random run almost surely lies in an open set whose specification
only depends on the qualitative behaviour of the pLTS. Here we
investigate similar issues for infinite pLTS. We first show that
this characterisation still holds for FF-diagnosability but
with a G-delta set instead of an open set and also for IF-
and IA-diagnosability when pLTS are finitely branching. We also
prove that surprisingly FA-diagnosability cannot be
characterised in this way even in the finitely branching case. Then
we apply our characterisations for a partially observable
probabilistic extension of visibly pushdown automata (POpVPA),
yielding EXPSPACE procedures for solving diagnosability problems.
In addition, we establish some computational lower bounds and show
that slight extensions of POpVPA lead to undecidability
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