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How to Specify and Verify the LongRun Average Behavior of Probabilistic Systems
 In Proc. LICS'98
, 1998
"... Longrun average properties of probabilistic systems refer to the average behavior of the system, measured over a period of time whose length diverges to infinity. These properties include many relevant performance and reliability indices, such as system throughput, average response time, and mean t ..."
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Cited by 61 (3 self)
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Longrun average properties of probabilistic systems refer to the average behavior of the system, measured over a period of time whose length diverges to infinity. These properties include many relevant performance and reliability indices, such as system throughput, average response time, and mean time between failures. In this paper, we argue that current formal specification methods cannot be used to specify longrun average properties of probabilistic systems. To enable the specification of these properties, we propose an approach based on the concept of experiments. Experiments are labeled graphs that can be used to describe behavior patterns of interest, such as the request for a resource followed by either a grant or a rejection. Experiments are meant to be performed infinitely often, and it is possible to specify their longrun average outcome or duration. We propose simple extensions of temporal logics based on experiments, and we present modelchecking algorithms for the verif...
Discounting the future in systems theory
 In Automata, Languages, and Programming, LNCS 2719
, 2003
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Monte Carlo Model Checking
 In Proc. of Tools and Algorithms for Construction and Analysis of Systems (TACAS 2005), volume 3440 of LNCS
, 2005
"... Abstract. We present MC 2, what we believe to be the first randomized, Monte Carlo algorithm for temporallogic model checking, the classical problem of deciding whether or not a property specified in temporal logic holds of a system specification. Given a specification S of a finitestate system, a ..."
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Cited by 59 (4 self)
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Abstract. We present MC 2, what we believe to be the first randomized, Monte Carlo algorithm for temporallogic model checking, the classical problem of deciding whether or not a property specified in temporal logic holds of a system specification. Given a specification S of a finitestate system, an LTL (Linear Temporal Logic) formula ϕ, and parameters ɛ and δ, MC 2 takes N = ln(δ) / ln(1 − ɛ) random samples (random walks ending in a cycle, i.e lassos) from the Büchi automaton B = BS × B¬ϕ to decide if L(B) = ∅. Should a sample reveal an accepting lasso l, MC 2 returns false with l as a witness. Otherwise, it returns true and reports that with probability less than δ, pZ < ɛ, where pZ is the expectation of an accepting lasso in B. It does so in time O(N · D) and space O(D), where D is B’s recurrence diameter, using a number of samples N that is optimal to within a constant factor. Our experimental results demonstrate that MC 2 is fast, memoryefficient, and scales very well.
Computing Minimum and Maximum Reachability Times in Probabilistic Systems
, 1999
"... A Markov decision process is a generalization of a Markov chain in which both probabilistic and nondeterministic choice coexist. Given a Markov decision process with costs associated with the transitions and a set of target states, the stochastic shortest path problem consists in computing the minim ..."
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Cited by 54 (2 self)
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A Markov decision process is a generalization of a Markov chain in which both probabilistic and nondeterministic choice coexist. Given a Markov decision process with costs associated with the transitions and a set of target states, the stochastic shortest path problem consists in computing the minimum expected cost of a control strategy that guarantees to reach the target. In this paper, we consider the classes of stochastic shortest path problems in which the costs are all nonnegative, or all nonpositive. Previously, these two classes of problems could be solved only under the assumption that the policies that minimize or maximize the expected cost also lead to the target with probability 1. This assumption does not necessarily hold for Markov decision processes that arise as model for distributed probabilistic systems. We present efficient methods for solving these two classes of problems without relying on additional assumptions. The methods are based on algorithms to transform th...
A Hierarchy of Probabilistic System Types
, 2003
"... We study various notions of probabilistic bisimulation from a coalgebraic point of view, accumulating in a hierarchy of probabilistic system types. In general, a natural transformation between two Setfunctors straightforwardly gives rise to a transformation of coalgebras for the respective functors ..."
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Cited by 53 (7 self)
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We study various notions of probabilistic bisimulation from a coalgebraic point of view, accumulating in a hierarchy of probabilistic system types. In general, a natural transformation between two Setfunctors straightforwardly gives rise to a transformation of coalgebras for the respective functors. This latter transformation preserves homomorphisms and thus bisimulations. For comparison of probabilistic system types we also need reflection of bisimulation. We build the hierarchy of probabilistic systems by exploiting the new result that the transformation also reflects bisimulation in case the natural transformation is componentwise injective and the first functor preserves weak pullbacks. Additionally, we illustrate the correspondence of concrete and coalgebraic bisimulation in the case of general Segalatype systems.
Compositional Methods for Probabilistic Systems
, 2001
"... We present a compositional tracebased model for probabilistic systems. The behavior of a system with probabilistic choice is a stochastic process, namely, a probability distribution on traces, or "bundle." Consequently, the semantics of a system with both nondeterministic and probabilisti ..."
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Cited by 50 (0 self)
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We present a compositional tracebased model for probabilistic systems. The behavior of a system with probabilistic choice is a stochastic process, namely, a probability distribution on traces, or "bundle." Consequently, the semantics of a system with both nondeterministic and probabilistic choice is a set of bundles. The bundles of a composite system can be obtained by combining the bundles of the components in a simple mathematical way. Re nement between systems is bundle containment. We achieve assumeguarantee compositionality for bundle semantics by introducing two scoping mechanisms. The first mechanism, which is standard in compositional modeling, distinguishes inputs from outputs and hidden state. The second mechanism, which arises in probabilistic systems, partitions the state into probabilistically independent regions.
Weak probabilistic anonymity
 INRIA FUTURS AND LIX
, 2005
"... Anonymity means that the identity of the user performing a certain action is maintained secret. The protocols for ensuring anonymity often use random mechanisms which can be described probabilistically. In this paper we propose a notion of weak probabilistic anonymity, where weak refers to the fact ..."
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Cited by 49 (11 self)
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Anonymity means that the identity of the user performing a certain action is maintained secret. The protocols for ensuring anonymity often use random mechanisms which can be described probabilistically. In this paper we propose a notion of weak probabilistic anonymity, where weak refers to the fact that some amount of probabilistic information may be revealed by the protocol. This information can be used by an observer to infer the likeliness that the action has been performed by a certain user. The aim of this work is to study the degree of anonymity that the protocol can still ensure, despite the leakage of information. We illustrate our ideas by using the example of the dining cryptographers with biased coins. We consider both the cases of nondeterministic and probabilistic users. Correspondingly, we propose two notions of weak anonymity and we investigate their respective dependencies on the biased factor of the coins.
Probabilistic model checking of the IEEE 802.11 wireless local area network protocol
 Proc. 2nd Joint International Workshop on Process Algebra and Probabilistic Methods, Performance Modeling and Verification (PAPM/PROBMIV’02), volume 2399 of LNCS
, 2002
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Probabilistic event structures and domains
 Concurrency Theory: 15th International Conference, CONCUR ’04 Proceedings, LNCS
, 2004
"... This paper investigates probability in the presence of causal dependence. More precisely, it studies the process model of probabilistic event structures. In their simplest form probabilistic choice is localised to cells at which immediate conflict arises; in which case probabilistic independence coi ..."
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Cited by 45 (10 self)
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This paper investigates probability in the presence of causal dependence. More precisely, it studies the process model of probabilistic event structures. In their simplest form probabilistic choice is localised to cells at which immediate conflict arises; in which case probabilistic independence coincides with causal independence. An event structure is associated with a domain—that of its configurations ordered by inclusion. In domain theory probabilistic processes are denoted by continuous valuations on a domain. A key result of this paper is a representation theorem showing how continuous valuations on the domain of a confusion free event structure correspond to the probabilistic event structures it supports. Via a notion of tests, probabilistic event structures are related to another approach to probabilistic processes, viz. Markov decision processes. Tests and morphisms of event structures point the way to a more general theory in which, for example, event structures need not be confusion free. 1
D.: Symmetry reduction for probabilistic model checking
 International Organization for Standardization. ISO Information Processing Systems  Data Communication HighLevel Data Link Control Procedure  Frame Structure. IS 3309
, 2006
"... Abstract. We present an approach for applying symmetry reduction techniques to probabilistic model checking, a formal verification method for the quantitative analysis of systems with stochastic characteristics. We target systems with a set of nontrivial, but interchangeable, components such as tho ..."
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Cited by 44 (13 self)
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Abstract. We present an approach for applying symmetry reduction techniques to probabilistic model checking, a formal verification method for the quantitative analysis of systems with stochastic characteristics. We target systems with a set of nontrivial, but interchangeable, components such as those which commonly arise in randomised distributed algorithms or probabilistic communication protocols. We show, for three types of probabilistic models, that symmetry reduction, similarly to the nonprobabilistic case, allows verification to instead be performed on a bisimilar quotient model which may be up to factorially smaller. We then propose an efficient algorithm for the construction of the quotient model using a symbolic implementation based on multiterminal binary decision diagrams (MTBDDs) and, using four large case studies, demonstrate that this approach offers not only a dramatic increase in the size of probabilistic model which can be quantitatively analysed but also a significant decrease in the corresponding runtimes. 1