Robust Multidimensional Mean-Payoff Games are Undecidable.

Mean-payoff games play a central role in quantitative synthesis and verification. In a single-dimensional game a weight is assigned to every transition and the objective of the protagonist is to assure a non-negative limit-average weight. In the multidimensional setting, a weight vector is assigned to every transition and the objective of the protagonist is to satisfy a boolean condition over the limit-average weight of each dimension, e.g., LimAvg(xi) < 0 V LimAvg(x2) >= 0 A LimAvg(x3) >= 0. We recently proved that when one of the players is restricted to finite-memory strategies then the decidability of determining the winner is inter-reducible with Hilbert's Tenth problem over rationals (a fundamental long-standing open problem). In this work we consider arbitrary (infinite-memory) strategies for both players and show that the problem is undecidable.