544 research outputs found
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
Comparative branching-time semantics for Markov chains
This paper presents various semantics in the branching-time spectrum of discrete-time and continuous-time Markov chains (DTMCs and CTMCs).\ud
Strong and weak bisimulation equivalence and simulation pre-orders are covered and are logically characterised in terms of the temporal logics PCTL (Probabilistic Computation Tree Logic) and CSL (Continuous Stochastic Logic). Apart from presenting various existing branching-time relations in a uniform manner, this paper presents the following new results: (i) strong simulation for CTMCs, (ii) weak simulation for CTMCs and DTMCs, (iii) logical characterizations thereof (including weak bisimulation for DTMCs), (iv) a relation between weak bisimulation and weak simulation equivalence, and (v) various connections between equivalences and pre-orders in the continuous- and discrete-time setting. The results are summarized in a branching-time spectrum for DTMCs and CTMCs elucidating their semantics as well as their relationship
Computing Distances between Probabilistic Automata
We present relaxed notions of simulation and bisimulation on Probabilistic
Automata (PA), that allow some error epsilon. When epsilon is zero we retrieve
the usual notions of bisimulation and simulation on PAs. We give logical
characterisations of these notions by choosing suitable logics which differ
from the elementary ones, L with negation and L without negation, by the modal
operator. Using flow networks, we show how to compute the relations in PTIME.
This allows the definition of an efficiently computable non-discounted distance
between the states of a PA. A natural modification of this distance is
introduced, to obtain a discounted distance, which weakens the influence of
long term transitions. We compare our notions of distance to others previously
defined and illustrate our approach on various examples. We also show that our
distance is not expansive with respect to process algebra operators. Although L
without negation is a suitable logic to characterise epsilon-(bi)simulation on
deterministic PAs, it is not for general PAs; interestingly, we prove that it
does characterise weaker notions, called a priori epsilon-(bi)simulation, which
we prove to be NP-difficult to decide.Comment: In Proceedings QAPL 2011, arXiv:1107.074
Strong, Weak and Branching Bisimulation for Transition Systems and Markov Reward Chains: A Unifying Matrix Approach
We first study labeled transition systems with explicit successful
termination. We establish the notions of strong, weak, and branching
bisimulation in terms of boolean matrix theory, introducing thus a novel and
powerful algebraic apparatus. Next we consider Markov reward chains which are
standardly presented in real matrix theory. By interpreting the obtained matrix
conditions for bisimulations in this setting, we automatically obtain the
definitions of strong, weak, and branching bisimulation for Markov reward
chains. The obtained strong and weak bisimulations are shown to coincide with
some existing notions, while the obtained branching bisimulation is new, but
its usefulness is questionable
A Definition Scheme for Quantitative Bisimulation
FuTS, state-to-function transition systems are generalizations of labeled
transition systems and of familiar notions of quantitative semantical models as
continuous-time Markov chains, interactive Markov chains, and Markov automata.
A general scheme for the definition of a notion of strong bisimulation
associated with a FuTS is proposed. It is shown that this notion of
bisimulation for a FuTS coincides with the coalgebraic notion of behavioral
equivalence associated to the functor on Set given by the type of the FuTS. For
a series of concrete quantitative semantical models the notion of bisimulation
as reported in the literature is proven to coincide with the notion of
quantitative bisimulation obtained from the scheme. The comparison includes
models with orthogonal behaviour, like interactive Markov chains, and with
multiple levels of behavior, like Markov automata. As a consequence of the
general result relating FuTS bisimulation and behavioral equivalence we obtain,
in a systematic way, a coalgebraic underpinning of all quantitative
bisimulations discussed.Comment: In Proceedings QAPL 2015, arXiv:1509.0816
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
Lumpability Abstractions of Rule-based Systems
The induction of a signaling pathway is characterized by transient complex
formation and mutual posttranslational modification of proteins. To faithfully
capture this combinatorial process in a mathematical model is an important
challenge in systems biology. Exploiting the limited context on which most
binding and modification events are conditioned, attempts have been made to
reduce the combinatorial complexity by quotienting the reachable set of
molecular species, into species aggregates while preserving the deterministic
semantics of the thermodynamic limit. Recently we proposed a quotienting that
also preserves the stochastic semantics and that is complete in the sense that
the semantics of individual species can be recovered from the aggregate
semantics. In this paper we prove that this quotienting yields a sufficient
condition for weak lumpability and that it gives rise to a backward Markov
bisimulation between the original and aggregated transition system. We
illustrate the framework on a case study of the EGF/insulin receptor crosstalk.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
- …