Luca Bortolussi

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In this paper we investigate a potential use of fluid approximation techniques in the context<lb>of stochastic model checking of CSL formulae. We focus on properties describing the behaviour<lb>of a single agent in a (large) population of agents, exploiting a limit result known also as<lb>fast simulation. In particular, we will approximate the behaviour of(More)
We present an application of stochastic Concurrent Constraint Programming (sCCP) for modeling biological systems. We provide a library of sCCP processes that can be used to describe straightforwardly biological networks. In the meanwhile, we show that sCCP proves to be a general and extensible framework, allowing to describe a wide class of dynamical(More)
In this paper we present an overview of the field of deterministic approximation of Markov processes, both in discrete and continuous time. We will discuss mean field approximation of discrete time Markov chains and fluid approximation of continuous time Markov chains, considering the cases in which the deterministic limit process lives in continuous time(More)
Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an(More)
We consider the problem of computing the satisfaction probability of a formula for stochastic models with parametric uncertainty. We show that this satisfaction probability is a smooth function of the model parameters. This enables us to devise a novel Bayesian statistical algorithm which performs model checking simultaneously for all values of the model(More)
We present Signal Spatio-Temporal Logic (SSTL), a modal logic that can be used to specify spatio-temporal properties in linear time and for a discrete space. The logic is equipped with a Boolean and a quantitative semantics, and with accompanying monitoring algorithms. As such, it is suitable for real-time verification of both white box and black box(More)
We consider a generic mean-field scenario, in which a sequence of population models, described by discretetime Markov chains (DTMCs), converges to a deterministic limit in discrete time. Under the assumption that the limit has a globally attracting equilibrium, the steady states of the sequence of DTMC models converge to the point-mass distribution(More)