Abstract
We propose a novel algorithm for the solution of mean-payoff games that merges together two seemingly unrelated concepts introduced in the context of parity games, small progress measures and quasi dominions. We show that the integration of the two notions can be highly beneficial and significantly speeds up convergence to the problem solution. Experiments show that the resulting algorithm performs orders of magnitude better than the asymptotically-best solution algorithm currently known, without sacrificing on the worst-case complexity.
Partially supported by GNCS 2019 & 2020 projects “Metodi Formali per Tecniche di Verifica Combinata” and “Ragionamento Strategico e Sintesi Automatica di Sistemi Multi-Agente”.
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Benerecetti, M., Dell’Erba, D., Mogavero, F. (2020). Solving Mean-Payoff Games via Quasi Dominions. In: Biere, A., Parker, D. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2020. Lecture Notes in Computer Science(), vol 12079. Springer, Cham. https://doi.org/10.1007/978-3-030-45237-7_18
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