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49
Measuring and synthesizing systems in probabilistic environments
 CoRR
"... Abstract. Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is pre ..."
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Abstract. Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimalsynthesis problem: given an omegaregular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification under the given input assumption, synthesize a system that optimizes the measured value. For safety specifications and measures that are defined by meanpayoff automata, the optimalsynthesis problem amounts to finding a strategy in a Markov decision process (MDP) that is optimal for a longrun average reward objective, which can be done in polynomial time. For general omegaregular specifications, the solution rests on a new, polynomialtime algorithm for computing optimal strategies in MDPs with meanpayoff parity objectives. We present some experimental results showing optimal systems that were automatically generated in this way. 1
Semantics for Structured Systems Modelling and Simulation
"... Simulation modelling is an important tool for exploring and reasoning about complex systems. Many supporting languages are available. Commonly occurring features of these languages are constructs capturing concepts such as process, resource, and location. We describe a mathematical framework that su ..."
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Cited by 18 (14 self)
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Simulation modelling is an important tool for exploring and reasoning about complex systems. Many supporting languages are available. Commonly occurring features of these languages are constructs capturing concepts such as process, resource, and location. We describe a mathematical framework that supports a modelling idiom based on these core concepts, and which adopts stochastic methods for representing the environments within which systems exist. We explain how this framework can be used to give a semantics to a simulation modelling language, Core Gnosis, that includes basic constructs for process, resource, and location. We include a brief discussion of a logic for reasoning about models that is compositional with respect to their structure. Our mathematical analysis of systems in terms of process, resource, location, and stochastic environment, together with a language that captures these concepts quite directly, yields an efficient and robust modelling framework within which natural mathematical reasoning about systems is captured.
Formalisms for Specifying Markovian Population Models
"... We compare several languages for specifying Markovian population models such as queuing networks and chemical reaction networks. These languages —matrix descriptions, stochastic Petri nets, stochastic process algebras, stoichiometric equations, and guarded command models — all describe continuoust ..."
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Cited by 13 (3 self)
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We compare several languages for specifying Markovian population models such as queuing networks and chemical reaction networks. These languages —matrix descriptions, stochastic Petri nets, stochastic process algebras, stoichiometric equations, and guarded command models — all describe continuoustime Markov chains, but they differ according to important properties, such as compositionality, expressiveness and succinctness, executability, ease of use, and the support they provide for checking the wellformedness of a model and for analyzing a model.
A timed calculus for wireless systems
 TCS
"... We propose a timed broadcasting process calculus for wireless systems where timeconsuming communications are exposed to collisions. The operational semantics of our calculus is given in terms of a labelled transition system. The calculus enjoys a number of desirable time properties such as (i) tim ..."
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Cited by 11 (4 self)
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We propose a timed broadcasting process calculus for wireless systems where timeconsuming communications are exposed to collisions. The operational semantics of our calculus is given in terms of a labelled transition system. The calculus enjoys a number of desirable time properties such as (i) time determinism: the passage of time is deterministic; (ii) patience: devices will wait indefinitely until they can communicate; (iii) maximal progress: data transmissions cannot be delayed, they must occur as soon as a possibility for communication arises. We use our calculus to model and study MAClayer protocols with a special emphasis on collisions and security. The main behavioural equality of our calculus is a timed variant of barbed congruence, a standard branchingtime and contextuallydefined program equivalence. As an efficient proof method for timed barbed congruence we define a labelled bisimilarity. We then apply our bisimulation prooftechnique to prove a number of algebraic laws. 1
Scalable Contextdependent Analysis of Emergency Egress Models
 UNDER CONSIDERATION FOR PUBLICATION IN FORMAL ASPECTS OF COMPUTING
"... Pervasive environments offer an increasing number of services to a large number of people moving within these environments, including timely information about where to go and when, and contextual information about the surrounding environment. This information may be conveyed to people through publ ..."
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Cited by 11 (5 self)
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Pervasive environments offer an increasing number of services to a large number of people moving within these environments, including timely information about where to go and when, and contextual information about the surrounding environment. This information may be conveyed to people through public displays or direct to a person’s mobile phone. People using these services interact with the system but they are also meeting other people and performing other activities as relevant opportunities arise. The design of such systems and the analysis of collective dynamic behaviour of people within them is a challenging problem. We present results on a novel usage of a scalable analysis technique in this context. We show the validity of an approach based on stochastic processalgebraic models by focussing on a representative example, i.e. emergency egress. The chosen case study has the advantage that detailed data is available from studies employing alternative analysis methods, making crossmethodology comparison possible. We also illustrate how realistic, contextdependent human behaviour, often observed in emergency egress, can naturally be embedded in the models, and how the effect of such behaviour on evacuation can be analysed in an efficient and scalable way. The proposed approach encompasses both the agent modelling viewpoint, as system behaviour emerges from specific (discrete) agent interaction, and the population viewpoint, when classes of homogeneous individuals are considered for a (continuous) approximation of overall system behaviour.
F.: Fortuna: Model checking priced probabilistic timed automata
 In: Proc. 7th Int. Conf. Quantitative Evaluation of SysTems (QEST’10
, 2010
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Modelling Nonlinear Crowd Dynamics in BioPEPA
"... Abstract. Emergent phenomena occur due to the pattern of nonlinear and distributed local interactions between the elements of a system over time. Surprisingly, agent based crowd models, in which the movement of each individual follows a limited set of simple rules, often reproduce quite closely th ..."
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Cited by 8 (3 self)
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Abstract. Emergent phenomena occur due to the pattern of nonlinear and distributed local interactions between the elements of a system over time. Surprisingly, agent based crowd models, in which the movement of each individual follows a limited set of simple rules, often reproduce quite closely the emergent behaviour of crowds that can be observed in reality. An example of such phenomena is the spontaneous selforganisation of drinking parties in the squares of cities in Spain, also known as “El Botellón ” [20]. We revisit this case study providing an elegant stochastic process algebraic model in BioPEPA amenable to several forms of analyses, among which simulation and fluid flow analysis. We show that a fluid flow approximation, i.e. a deterministic reading of the average behaviour of the system, can provide an alternative and efficient way to study the same emergent behaviour as that explored in [20] where simulation was used instead. Besides empirical evidence, also an analytical justification is provided for the good correspondence found between simulation results and the fluid flow approximation.
Efficient Parallel Statistical Model Checking of Biochemical Networks
 In Parallel and Distributed Methods in verifiCation, volume 14 of EPTCS
, 2009
"... We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out ..."
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We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property φ holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by onthefly verification of φ which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of φ to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture. 1
Simulationbased ctmc model checking: an emprical evaluation
 In Proc. QEST’09
, 2009
"... This paper provides an experimental study of the efficiency of simulationbased modelchecking algorithms for continuoustime Markov chains by comparing: MRMC – the only tool that implements (new) confidenceintervalbased algorithms for verification of all main CSL formulae; Ymer – that allows fo ..."
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Cited by 6 (0 self)
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This paper provides an experimental study of the efficiency of simulationbased modelchecking algorithms for continuoustime Markov chains by comparing: MRMC – the only tool that implements (new) confidenceintervalbased algorithms for verification of all main CSL formulae; Ymer – that allows for verification of timebounded and timeinterval until using sequential acceptance sampling; and VESTA – that can verify timebounded and unbounded until by means of simple hypothesis testing. The study shows that MRMC provides the most accurate verification results. Ymer and VESTA, unlike MRMC, have almost constant memory consumption. Ymer requires the least number of observations to assess the modelchecking problem, but MRMC is mostly the fastest. This indicates that the tools ’ efficiency does not so much depend on sampling but is rather determined by extra computations. 1.
On the numerical analysis of stochastic LotkaVolterra models, in
 Proc. of the Workshop on Computer Aspects of Numerical Algorithms (CANA10
"... Abstract—The stochastic LotkaVolterra model is an infinite Markov population model that has applications in various life science domains. Its analysis is challenging since, besides an infinite state space with unbounded rates, it shows strongly fluctuating dynamics and becomes unstable in the long ..."
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Abstract—The stochastic LotkaVolterra model is an infinite Markov population model that has applications in various life science domains. Its analysis is challenging since, besides an infinite state space with unbounded rates, it shows strongly fluctuating dynamics and becomes unstable in the longrun. Traditional numerical methods are therefore not appropriate to solve the system. Here, we suggest adaptations and combinations of traditional methods that yield fast and accurate solutions for certain parameter ranges of the stochastic LotkaVolterra model. We substantiate our theoretical investigations with a comparison based on experimental results. I.