3 papers
cs.AI2020
Approximating Euclidean by Imprecise Markov Decision Processes
Manfred Jaeger, Giorgio Bacci, Giovanni Bacci +2
Euclidean Markov decision processes are a powerful tool for modeling control problems under uncertainty over continuous domains. Finite state imprecise, Markov decision processes c…
cs.LG2019
L*-Based Learning of Markov Decision Processes (Extended Version)
Martin Tappler, Bernhard K. Aichernig, Giovanni Bacci +2
Automata learning techniques automatically generate system models from test observations. These techniques usually fall into two categories: passive and active. Passive learning us…
cs.FL2018
On the Metric-based Approximate Minimization of Markov Chains
Giovanni Bacci, Giorgio Bacci, Kim G. Larsen +1
In this paper, we address the approximate minimization problem of Markov Chains (MCs) from a behavioral metric-based perspective. Specifically, given a finite MC and a positive int…