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Playing Games with your PET: Extending the Partial Exploration Tool to Stochastic Games
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We present version 2.0 of the Partial Exploration Tool (PET), a tool for verification of probabilistic systems. We extend the previous version by adding support for stochastic games, based on a recent unified framework for sound value iteration algorithms. Thereby, PET2 is the first tool implementing a sound and efficient approach for solving stochastic games with objectives of the type reachability/safety and mean payoff. We complement this approach by developing and implementing a partial-exploration based variant for all three objectives. Our experimental evaluation shows that PET2 offers the most efficient partial-exploration based algorithm and is the most viable tool on SGs, even outperforming unsound tools.
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Solving Robust Markov Decision Processes: Generic, Reliable, Efficient
Robust MDPs can be solved generically with implicit value iteration that provides anytime precision guarantees for many uncertainty sets, including L2 balls.
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