Nicolás Wolovick

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Continuous-time Markov decision process are an important variant of labelled transition systems having nondeterminism through labels and stochasticity through exponential fire-time distributions. Nondeterministic choices are resolved using the notion of a scheduler. In this paper we characterize the class of measurable schedulers, which is the most general(More)
Model-based test derivation for real-time system has been proven to be a hard problem for exhaustive test suites. Therefore, techniques for real-time testing do not aim to exhaustiveness but Instead respond to particular coverage criteria. Since it Is not feasible to generate complete test suites for real time systems, It IsI very Important that test case(More)
Dealing with the interplay of randomness and continuous time is important for the formal verification of many real systems. Considering both facets is especially important for wireless sensor networks, distributed control applications, and many other systems of growing importance. An important traditional design and verification goal for such systems is to(More)
We extend the theory of labeled Markov processes with internal nondeterminism, a fundamental concept for the further development of a process theory with abstraction on nondeterministic continuous probabilistic systems. We define nondeterministic labeled Markov processes (NLMP) and provide three definition of bisimulations: a bisimulation following a(More)
Probabilistic algorithms are recognized for their simplicity and speed. A canonical example is the Miller-Rabin primality test algorithm. It is simple and achieves high accuracy with a small amount of computation. In this paper, we present two verification exercises of this algorithm using two different approaches: one being a probabilistic extension of the(More)
The weakest pre-expectation calculus [20] has been proved to be a mature theory to analyze quantitative properties of probabilistic and nondeterministic programs. We present an automatic method for proving quantitative linear properties on any denumerable state space using iterative backwards fixed point calculation in the general framework of abstract(More)