Markov decision processes (MDPs) offer a popular mathematical tool for planning and
learning in the presence of uncertainty (Boutilier, Dean, & Hanks 1999). MDPs are a …

In contrast to its wealth of applications in mathematics, the Kantorovich metric started to be
noticed in computer science only in recent years. We give a brief survey of its applications in …

In recent years, various metrics have been developed for measuring the behavioral similarity
of states in probabilistic transition systems [J. Desharnais et al., Proceedings of …

Discounting the future means that the value, today, of a unit payoffis 1 if the payoffo ccurs
today, a if it occurs tomorrow, a 2 if it occurs the day after tomorrow, and so on, for some real …

We tackle the problem of non robustness of simulation and bisimulation when dealing with
probabilistic processes. It is important to ignore tiny deviations in probabilities because …

Program sensitivity measures how robust a program is to small changes in its input, and is a
fundamental notion in domains ranging from differential privacy to cyber-physical systems. A …

We develop a model of parametric probabilistic transition Systems (PPTSs), where
probabilities associated with transitions may be parameters. We show how to find instances …

In contrast with the existing approaches to exact bisimulation for fuzzy systems, we introduce
a robust notion of behavioral distance to measure the behavioral similarity of …

The bisimilarity pseudometric based on the Kantorovich lifting is one of the most popular
metrics for probabilistic processes proposed in the literature. However, its application in …

We present metrics for measuring state similarity in Markov decision processes (MDPs) with
infinitely many states, including MDPs with continuous state spaces. Such metrics provide a …