33 citations · 73 across the 12 of their papers we have counts for
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eess.SY2016★ 2 cited
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Michalis Michaelides, Dimitrios Milios, Jane Hillston +1
Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to…
eess.SY2016★ 3 cited
Policy learning for time-bounded reachability in Continuous-Time Markov Decision Processes via doubly-stochastic gradient ascent
Ezio Bartocci, Luca Bortolussi, Tomǎš Brázdil +2
Continuous-time Markov decision processes are an important class of models in a wide range of applications, ranging from cyber-physical systems to synthetic biology. A central prob…