658 research outputs found
Logical Characterizations of Behavioral Relations on Transition Systems of Probability Distributions
Probabilistic nondeterministic processes are commonly modeled as probabilistic LTSs (PLTSs). A number of logical characterizations of the main behavioral relations on PLTSs have been studied. In particular, Parma and Segala [2007] and Hermanns et al. [2011] define a probabilistic Hennessy-Milner logic interpreted over probability distributions, whose corresponding logical equivalence/preorder when restricted to Dirac distributions coincide with standard bisimulation/simulation between the states of a PLTS. This result is here extended by studying the full logical equivalence/preorder between (possibly non-Dirac) distributions in terms of a notion of bisimulation/simulation defined on a LTS whose states are distributions (dLTS). We show that the well-known spectrum of behavioral relations on nonprobabilistic LTSs as well as their corresponding logical characterizations in terms of Hennessy-Milner logic scales to the probabilistic setting when considering dLTSs
A short note on Simulation and Abstraction
This short note is written in celebration of David Schmidt's sixtieth
birthday. He has now been active in the program analysis research community for
over thirty years and we have enjoyed many interactions with him. His work on
characterising simulations between Kripke structures using Galois connections
was particularly influential in our own work on using probabilistic abstract
interpretation to study Larsen and Skou's notion of probabilistic bisimulation.
We briefly review this work and discuss some recent applications of these ideas
in a variety of different application areas.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
Process algebra for performance evaluation
This paper surveys the theoretical developments in the field of stochastic process algebras, process algebras where action occurrences may be subject to a delay that is determined by a random variable. A huge class of resource-sharing systems – like large-scale computers, client–server architectures, networks – can accurately be described using such stochastic specification formalisms. The main emphasis of this paper is the treatment of operational semantics, notions of equivalence, and (sound and complete) axiomatisations of these equivalences for different types of Markovian process algebras, where delays are governed by exponential distributions. Starting from a simple actionless algebra for describing time-homogeneous continuous-time Markov chains, we consider the integration of actions and random delays both as a single entity (like in known Markovian process algebras like TIPP, PEPA and EMPA) and as separate entities (like in the timed process algebras timed CSP and TCCS). In total we consider four related calculi and investigate their relationship to existing Markovian process algebras. We also briefly indicate how one can profit from the separation of time and actions when incorporating more general, non-Markovian distributions
Algorithms for Game Metrics
Simulation and bisimulation metrics for stochastic systems provide a
quantitative generalization of the classical simulation and bisimulation
relations. These metrics capture the similarity of states with respect to
quantitative specifications written in the quantitative {\mu}-calculus and
related probabilistic logics. We first show that the metrics provide a bound
for the difference in long-run average and discounted average behavior across
states, indicating that the metrics can be used both in system verification,
and in performance evaluation. For turn-based games and MDPs, we provide a
polynomial-time algorithm for the computation of the one-step metric distance
between states. The algorithm is based on linear programming; it improves on
the previous known exponential-time algorithm based on a reduction to the
theory of reals. We then present PSPACE algorithms for both the decision
problem and the problem of approximating the metric distance between two
states, matching the best known algorithms for Markov chains. For the
bisimulation kernel of the metric our algorithm works in time O(n^4) for both
turn-based games and MDPs; improving the previously best known O(n^9\cdot
log(n)) time algorithm for MDPs. For a concurrent game G, we show that
computing the exact distance between states is at least as hard as computing
the value of concurrent reachability games and the square-root-sum problem in
computational geometry. We show that checking whether the metric distance is
bounded by a rational r, can be done via a reduction to the theory of real
closed fields, involving a formula with three quantifier alternations, yielding
O(|G|^O(|G|^5)) time complexity, improving the previously known reduction,
which yielded O(|G|^O(|G|^7)) time complexity. These algorithms can be iterated
to approximate the metrics using binary search.Comment: 27 pages. Full version of the paper accepted at FSTTCS 200
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
An Algorithm for Probabilistic Alternating Simulation
In probabilistic game structures, probabilistic alternating simulation
(PA-simulation) relations preserve formulas defined in probabilistic
alternating-time temporal logic with respect to the behaviour of a subset of
players. We propose a partition based algorithm for computing the largest
PA-simulation, which is to our knowledge the first such algorithm that works in
polynomial time, by extending the generalised coarsest partition problem (GCPP)
in a game-based setting with mixed strategies. The algorithm has higher
complexities than those in the literature for non-probabilistic simulation and
probabilistic simulation without mixed actions, but slightly improves the
existing result for computing probabilistic simulation with respect to mixed
actions.Comment: We've fixed a problem in the SOFSEM'12 conference versio
Syntactic Markovian Bisimulation for Chemical Reaction Networks
In chemical reaction networks (CRNs) with stochastic semantics based on
continuous-time Markov chains (CTMCs), the typically large populations of
species cause combinatorially large state spaces. This makes the analysis very
difficult in practice and represents the major bottleneck for the applicability
of minimization techniques based, for instance, on lumpability. In this paper
we present syntactic Markovian bisimulation (SMB), a notion of bisimulation
developed in the Larsen-Skou style of probabilistic bisimulation, defined over
the structure of a CRN rather than over its underlying CTMC. SMB identifies a
lumpable partition of the CTMC state space a priori, in the sense that it is an
equivalence relation over species implying that two CTMC states are lumpable
when they are invariant with respect to the total population of species within
the same equivalence class. We develop an efficient partition-refinement
algorithm which computes the largest SMB of a CRN in polynomial time in the
number of species and reactions. We also provide an algorithm for obtaining a
quotient network from an SMB that induces the lumped CTMC directly, thus
avoiding the generation of the state space of the original CRN altogether. In
practice, we show that SMB allows significant reductions in a number of models
from the literature. Finally, we study SMB with respect to the deterministic
semantics of CRNs based on ordinary differential equations (ODEs), where each
equation gives the time-course evolution of the concentration of a species. SMB
implies forward CRN bisimulation, a recently developed behavioral notion of
equivalence for the ODE semantics, in an analogous sense: it yields a smaller
ODE system that keeps track of the sums of the solutions for equivalent
species.Comment: Extended version (with proofs), of the corresponding paper published
at KimFest 2017 (http://kimfest.cs.aau.dk/
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