22,894 research outputs found
Testing Reactive Probabilistic Processes
We define a testing equivalence in the spirit of De Nicola and Hennessy for
reactive probabilistic processes, i.e. for processes where the internal
nondeterminism is due to random behaviour. We characterize the testing
equivalence in terms of ready-traces. From the characterization it follows that
the equivalence is insensitive to the exact moment in time in which an internal
probabilistic choice occurs, which is inherent from the original testing
equivalence of De Nicola and Hennessy. We also show decidability of the testing
equivalence for finite systems for which the complete model may not be known
A uniform framework for modelling nondeterministic, probabilistic, stochastic, or mixed processes and their behavioral equivalences
Labeled transition systems are typically used as behavioral models of concurrent processes, and the labeled transitions define the a one-step state-to-state reachability relation. This model can be made generalized by modifying the transition relation to associate a state reachability distribution, rather than a single target state, with any pair of source state and transition label. The state reachability distribution becomes a function mapping each possible target state to a value that expresses the degree of one-step reachability of that state. Values are taken from a preordered set equipped with a minimum that denotes unreachability. By selecting suitable preordered sets, the resulting model, called ULTraS from Uniform Labeled Transition System, can be specialized to capture well-known models of fully nondeterministic processes (LTS), fully
probabilistic processes (ADTMC), fully stochastic processes (ACTMC), and of nondeterministic and probabilistic (MDP) or nondeterministic and stochastic (CTMDP) processes. This uniform treatment of different behavioral models extends to behavioral equivalences. These can be defined on ULTraS by relying on appropriate measure functions that expresses the degree of reachability of a set of states when performing
single-step or multi-step computations. It is shown that the specializations of bisimulation, trace, and testing
equivalences for the different classes of ULTraS coincide with the behavioral equivalences defined in the literature over traditional models
Disjunctive Probabilistic Modal Logic is Enough for Bisimilarity on Reactive Probabilistic Systems
Larsen and Skou characterized probabilistic bisimilarity over reactive
probabilistic systems with a logic including true, negation, conjunction, and a
diamond modality decorated with a probabilistic lower bound. Later on,
Desharnais, Edalat, and Panangaden showed that negation is not necessary to
characterize the same equivalence. In this paper, we prove that the logical
characterization holds also when conjunction is replaced by disjunction, with
negation still being not necessary. To this end, we introduce reactive
probabilistic trees, a fully abstract model for reactive probabilistic systems
that allows us to demonstrate expressiveness of the disjunctive probabilistic
modal logic, as well as of the previously mentioned logics, by means of a
compactness argument.Comment: Aligned content with version accepted at ICTCS 2016: fixed minor
typos, added reference, improved definitions in Section 3. Still 10 pages in
sigplanconf forma
Learning Markov Decision Processes for Model Checking
Constructing an accurate system model for formal model verification can be
both resource demanding and time-consuming. To alleviate this shortcoming,
algorithms have been proposed for automatically learning system models based on
observed system behaviors. In this paper we extend the algorithm on learning
probabilistic automata to reactive systems, where the observed system behavior
is in the form of alternating sequences of inputs and outputs. We propose an
algorithm for automatically learning a deterministic labeled Markov decision
process model from the observed behavior of a reactive system. The proposed
learning algorithm is adapted from algorithms for learning deterministic
probabilistic finite automata, and extended to include both probabilistic and
nondeterministic transitions. The algorithm is empirically analyzed and
evaluated by learning system models of slot machines. The evaluation is
performed by analyzing the probabilistic linear temporal logic properties of
the system as well as by analyzing the schedulers, in particular the optimal
schedulers, induced by the learned models.Comment: In Proceedings QFM 2012, arXiv:1212.345
Voltage harmonic reduction for randomly time-varying source characteristics and voltage harmonics
This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. Copyright @ 2006 IEEEPotential applications of probabilistic modeling of current and voltage harmonics concern many aspects of power system engineering as accurate prediction of power system harmonic behavior provides important information to utility companies and equipment designers. In this paper, a method of reducing the expected value of the total voltage harmonic distortion for a specified range of source impedance values at different buses by using LC compensators, where it is desired to maintain a given power factor at a specified value, is presented. The criterion is based on mean value estimation of source and load characteristics, which are enabled by sampling measurements performed on the examined electrical plant as well as statistical analysis
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