Andrea Turrini

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We introduce the web-based model checker ISCASMC for probabilistic systems (see This Java application offers an easy-to-use web interface for the evaluation of Markov chains and decision processes against PCTL and PCTL * specifications. Compared to PRISM or MRMC, ISCASMC is particularly efficient in evaluating the(More)
We consider bisimulation and weak bisimulation relations in the context of the Labeled Markov Chains of Hans-son and Jonsson, the Concurrent Labeled Markov Chains of Philippou, Lee, and Sokolsky, and the Probabilistic Au-tomata of Segala. We identify a taxonomy of bisimulation relations that captures the existing definitions for each one of the three(More)
Deciding in an efficient way weak probabilistic bisimulation in the context of probabilistic au-tomata is an open problem for about a decade. In this work we close this problem by proposing a procedure that checks in polynomial time the existence of a weak combined transition satisfying the step condition of the bisimulation. This enables us to arrive at a(More)
We study simulation relations for probabilistic automata that require transitions to be matched up to negligible sets provided that computation lengths are polynomially bounded. These relations are meant to provide rigorous grounds to parts of correctness proofs for cryptographic protocols that are usually carried out by semi-formal arguments. We illustrate(More)
One of the prevailing ideas in applied concurrency theory and verification is the concept of automata minimization with respect to strong or weak bisimilarity. The minimal automata can be seen as canonical representations of the behaviour modulo the bisimilarity considered. Together with congruence results wrt. process algebraic operators, this can be(More)
Probabilistic automata (PA) are a prominent compositional concur-rency model. As a way to justify property-preserving abstractions, in the last years, bisimulation relations over probability distributions have been proposed both in the strong and the weak setting. Different to the usual bisimulation relations , which are defined over states, an algorithmic(More)
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The classical planning problem can be enriched with quantitative and qualitative user-defined preferences on how the system behaves on achieving the goal. In this paper, we propose the probabilistic preference planning problem for Markov decision processes, where the preferences are based on an enriched probabilistic LTL-style logic. We develop P4Solver, an(More)
The bottleneck in the quantitative analysis of Markov chains and Markov decision processes against specifications given in LTL or as some form of nondeterministic Büchi automata is the inclusion of a determinisation step of the automaton under consideration. Such determinisation constructions are notoriously difficult and cumbersome to implement. In this(More)