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Robust Multidimensional Mean-Payoff Games are Undecidable

  • Yaron Velner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9034)

Abstract

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(x 1) ≤ 0 ∨ LimAvg(x 2) ≥ 0 ∧ LimAvg(x 3) ≥ 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.

Keywords

Simulation Step Winning Strategy Boolean Formula Left Transition Game Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yaron Velner
    • 1
  1. 1.The Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael

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