The papers “Accurately Computing Expected Visiting Times and Stationary Distributions in Markov Chains” by Hannah Mertens, Joost-Pieter Katoen, Tim Quatmann and Tobias Winkler and “Learning Explainable and Better Performing Representations of POMDP Strategies” by Alexander Bork, Debraj Chakraborty, Kush Grover, Jan Kretinsky and Stefanie Mohr have both been accepted at TACAS 2024 in Luxemburg.

The first paper presents new and efficient algorithms for computing expected visited times in (discrete- and continuous-time) Markov chains and shows how this can be used to accelerate computing various other measures such as stationary distributions.

The second paper considers computing succinct and comprehensible representations of strategies in partially observable Markov decision processes. It combines probabilistic model checking with automata learning techniques.