Results 11  20
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40
Stochastic transition systems for continuous state spaces and nondeterminism
 In FoSSaCS’05, LNCS 3441
, 2005
"... Abstract. We study the interaction between nondeterministic and probabilistic behaviour in systems with continuous state spaces, arbitrary probability distributions and uncountable branching. Models of such systems have been proposed previously. Here, we introduce a model that extends probabilistic ..."
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Cited by 13 (4 self)
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Abstract. We study the interaction between nondeterministic and probabilistic behaviour in systems with continuous state spaces, arbitrary probability distributions and uncountable branching. Models of such systems have been proposed previously. Here, we introduce a model that extends probabilistic automata to the continuous setting. We identify the class of schedulers that ensures measurability properties on executions, and show that such measurability properties are preserved by parallel composition. Finally, we demonstrate how these results allow us to define an alternative notion of weak bisimulation in our model. 1
Symbolic Computation of Maximal Probabilistic Reachability
 In Proc. CONCUR'01, volume 2154 of LNCS
, 2001
"... We study the maximal reachability probability problem for infinitestate systems featuring both nondeterministic and probabilistic choice. The problem involves the computation of the maximal probability of reaching a given set of states, and underlies decision procedures for the automatic verificati ..."
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Cited by 12 (8 self)
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We study the maximal reachability probability problem for infinitestate systems featuring both nondeterministic and probabilistic choice. The problem involves the computation of the maximal probability of reaching a given set of states, and underlies decision procedures for the automatic verification of probabilistic systems. We extend the framework of symbolic transition systems, which equips an infinitestate system with an algebra of symbolic operators on its state space, with a symbolic encoding of probabilistic transitions to obtain a model for an infinitestate probabilistic system called a symbolic probabilistic system.
Bounded Model Checking for GSMP Models of Stochastic Realtime Systems
 In Proc. of HSCC’06, LNCS 3927
, 2006
"... Model checking is a popular algorithmic verification technique for checking temporal requirements of mathematical models of systems. In this paper, we consider the problem of verifying bounded reachability properties of stochastic realtime systems modeled as generalized semiMarkov processes (GS ..."
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Cited by 9 (1 self)
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Model checking is a popular algorithmic verification technique for checking temporal requirements of mathematical models of systems. In this paper, we consider the problem of verifying bounded reachability properties of stochastic realtime systems modeled as generalized semiMarkov processes (GSMP).
Extending PDDL to Model Stochastic Decision Processes
, 2003
"... We present an extension of PDDL for modeling stochastic decision processes. Our domain description language allows the specification of actions with probabilistic effects, exogenous events, and actions and events with delayed effects. The result is a language that can be used to specify stochast ..."
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Cited by 9 (0 self)
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We present an extension of PDDL for modeling stochastic decision processes. Our domain description language allows the specification of actions with probabilistic effects, exogenous events, and actions and events with delayed effects. The result is a language that can be used to specify stochastic decision processes, both discretetime and continuoustime, of varying complexity. We also propose the use of established logic formalisms, taken from the model checking community, for specifying probabilistic temporally extended goals.
On ZoneBased Analysis of Duration Probabilistic Automata
"... We propose an extension of the zonebased algorithmics for analyzing timed automata to handle systems where timing uncertainty is considered as probabilistic rather than settheoretic. We study duration probabilistic automata (DPA), expressing multiple parallel processes admitting memoryfull continu ..."
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Cited by 6 (1 self)
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We propose an extension of the zonebased algorithmics for analyzing timed automata to handle systems where timing uncertainty is considered as probabilistic rather than settheoretic. We study duration probabilistic automata (DPA), expressing multiple parallel processes admitting memoryfull continuouslydistributed durations. For this model we develop an extension of the zonebased forward reachability algorithm whose successor operator is a density transformer, thus providing a solution to verification and performance evaluation problems concerning acyclic DPA (or the boundedhorizon behavior of cyclic DPA). 1
Probabilistic Timed Behavior Trees
"... Abstract The Behavior Tree notation has been developed as a method for systematically and traceably capturing user requirements. In this paper we extend the notation with probabilistic behaviour, so that reliability, performance, and other dependability properties can be expressed. The semantics of ..."
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Cited by 5 (2 self)
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Abstract The Behavior Tree notation has been developed as a method for systematically and traceably capturing user requirements. In this paper we extend the notation with probabilistic behaviour, so that reliability, performance, and other dependability properties can be expressed. The semantics of probabilistic timed Behavior Trees is given by mapping them to probabilistic timed automata. We gain advantages for requirements capture using Behavior Trees by incorporating into the notation an existing elegant specification formalism (probabilistic timed automata) which has tool support for formal analysis of probabilistic user requirements.
Almostsure modelchecking of reactive timed automata
 In QEST’12
, 2012
"... Abstract—We consider the model of stochastic timed automata, a model in which both delays and discrete choices are made probabilistically. We are interested in the almostsure modelchecking problem, which asks whether the automaton satisfies a given property with probability 1. While this problem wa ..."
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Cited by 3 (0 self)
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Abstract—We consider the model of stochastic timed automata, a model in which both delays and discrete choices are made probabilistically. We are interested in the almostsure modelchecking problem, which asks whether the automaton satisfies a given property with probability 1. While this problem was shown decidable for singleclock automata few years ago, it was also proven that the algorithm for this decidability result could not be used for general timed automata. In this paper we describe the subclass of reactive timed automata, and we prove decidability of the almostsure modelchecking problem under that restriction. Decidability relies on the fact that this model is almostsurely fair. As a desirable property of real systems, we show that reactive automata are almostsurely nonZeno. Finally we show that the almostsure modelchecking problem can be decided for specifications given as deterministic timed automata. I.
Timed Automata for the Development of RealTime Systems
, 2011
"... Timed automata are a popular formalism to model realtime systems. They were introduced two decades ago to support formal verification. Since then they have also been used for other purposes and a large has been introduced to be able to deal with the many different kinds of requirements of realtime ..."
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Cited by 2 (0 self)
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Timed automata are a popular formalism to model realtime systems. They were introduced two decades ago to support formal verification. Since then they have also been used for other purposes and a large has been introduced to be able to deal with the many different kinds of requirements of realtime system. This paper presents a fairly comprehensive survey, comprised of eighty variants of timed automata. The paper classifies all these eighty variants of timed automata in an effort to determine current developments. It uses analysis techniques, formal properties, and decision problems to draw distinctions between different versions. Moreover, the paper discusses the challenges behind using a timed automata specification to derive an implementation of a working realtime system and presents some solutions. Finally, the paper lists and classifies forty tools supporting timed automata. The paper does not only discuss many variants and their supporting concepts (e.g., closure properties, decision problems), techniques (e.g., for analysis), and tools, but it also attempts to help the reader navigate the vast literature in the field, to highlight differences and similarities between variants, and to reveal research trends and promising avenues for future exploration.
J.Sproston. Verifying soft deadlines with probabilistic timed automata
 In Proc. of the Workshop on Advances in Verification (WAVe
, 2000
"... Abstract. This paper describes work in progess performed as part of an ongoing project aimed at the development of theoretical foundations and model checking algorithms for the verification of soft deadlines in timed systems, that is, properties such as “there is a 90 % chance that the message will ..."
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Abstract. This paper describes work in progess performed as part of an ongoing project aimed at the development of theoretical foundations and model checking algorithms for the verification of soft deadlines in timed systems, that is, properties such as “there is a 90 % chance that the message will be delivered within 5 time units”. The research is focussed on the probabilistic timed automata model [11], an extension of timed automata [3], and includes: model checking of discreteprobabilistic automata based on the region graph construction [11]; symbolic methods based on forwards and backwards reachability [10,13]; and the continuous probabilistic timed automata [12]. 1
Probabilistic Model Checking of nonMarkovian Models with Concurrent Generally Distributed Timers
"... Abstract—In the analysis of stochastic concurrent timed models, probabilistic model checking combines qualitative identification of feasible behaviors with quantitative evaluation of their probability. If the stochastic process underlying the model is a Continuous Time Markov Chain (CTMC), the probl ..."
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Abstract—In the analysis of stochastic concurrent timed models, probabilistic model checking combines qualitative identification of feasible behaviors with quantitative evaluation of their probability. If the stochastic process underlying the model is a Continuous Time Markov Chain (CTMC), the problem can be solved by leveraging on the memoryless property of exponential distributions. However, when multiple generally distributed timers can be concurrently enabled, the underlying process may become a Generalized Semi Markov Process (GSMP) for which simulation is often advocated as the only viable approach to evaluation. The method of stochastic state classes provides a means for the analysis of models belonging to this class, that relies on the derivation of multivariate joint distributions of times to fire supported over Difference Bounds Matrix (DBM) zones. Transient stochastic state classes extend the approach with an additional age clock associating each state with the distribution of the time at which it can be reached. We show how transient stochastic state classes can be used to perform bounded probabilistic model checking also for models with underlying GSMPs, and we characterize the conditions for termination of the resulting algorithm, both in exact and approximate evaluation. We also show how the number of classes enumerated to complete the analysis can be largely reduced through a lookahead in the nondeterministic state class graph of reachable DBM zones. As notable traits, the proposed technique accepts efficient implementation based on DBM zones without requiring the split of domains in regions, and it expresses the bound in terms of a bilateral constraint on the elapsed time without requiring assumptions on the discrete number of executed transitions. Experimental results based on a preliminary implementation in the Oris tool are reported. Index Terms—Generalized SemiMarkov Process, NonMarkovian Stochastic Petri net, probabilistic model checking, stochastic state class, DBM zones. I.