Giovanni Bacci

Computing Behavioral Distances, Compositionally

We propose a general definition of composition operator on Markov Decision Processes with rewards (MDPs) and identify a well behaved class of operators, called safe, that are guaranteed to be non-extensive w.r.t. the bisimilarity pseudometrics of Ferns et al. [10], which measure behavioral similarities between MDPs. For MDPs built using safe/non-extensive operators, we present the first method that exploits the structure of the system for (exactly) computing the bisimilarity distance on MDPs. Experimental results show significant improvements upon the non-compositional technique.

On the Total Variation Distance of Semi-Markov Chains

Semi-Markov chains (SMCs) are continuous-time probabilistic transition systems where the residence time on states is governed by generic distributions on the positive real line.

Converging from Branching to Linear Metrics onźMarkov Chains

We study the strong and strutter trace distances on Markov chains MCs. Our interest in these metrics is motivated by their relation to the probabilistic

On the metric-based approximate minimization of Markov Chains

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Optimal and Robust Controller Synthesis Using Energy Timed Automata with Uncertainty

-model checking problem: we prove that they correspond to the maximal differences in the probability of satisfying the same

Models, Algorithms, Logics and Tools

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