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.
CITATION STYLE
Benerecetti, M., Dell’Erba, D., & Mogavero, F. (2020). Solving Mean-Payoff Games via Quasi Dominions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12079 LNCS, pp. 289–306). Springer. https://doi.org/10.1007/978-3-030-45237-7_18
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