Nicolas Markey

Résumé

Two-player quantitative zero-sum games provide a natural framework to synthesize controllers with performance guarantees for reactive systems within an uncontrollable environment. Classical settings include mean-payoff games, where the objective is to optimize the long-run average gain per action, and energy games, where the system has to avoid running out of energy.

We study average-energy games, where the goal is to optimize the long-run average of the accumulated energy. We show that this objective arises naturally in several applications, and that it yields interesting connections with previous concepts in the literature. We prove that deciding the winner in such games is in NP∩coNP and at least as hard as solving mean-payoff games, and we establish that memoryless strategies suffice to win. We also consider the case where the system has to minimize the average-energy while maintaining the accumulated energy within predefined bounds at all times: this corresponds to operating with a finite-capacity storage for energy. We give results for one-player and two-player games, and establish complexity bounds and memory requirements.

@inproceedings{gandalf2015-BMRLL,
  author =              {Bouyer, Patricia and Markey, Nicolas and Randour,
                         Mickael and Larsen, Kim Guldstrand and Laursen,
                         Simon},
  title =               {Average-energy games},
  editor =              {Esparza, Javier and Tronci, Enrico},
  booktitle =           {{P}roceedings of the 6th {I}nternational {S}ymposium
                         on {G}ames, {A}utomata, {L}ogics and {F}ormal
                         {V}erification ({GandALF}'15)},
  acronym =             {{GandALF}'15},
  series =              {Electronic Proceedings in Theoretical Computer
                         Science},
  volume =              {193},
  pages =               {1-15},
  year =                {2015},
  month =               sep,
  doi =                 {10.4204/EPTCS.193.1},
  abstract =            {Two-player quantitative zero-sum games provide a
                         natural framework to synthesize controllers with
                         performance guarantees for reactive systems within
                         an uncontrollable environment. Classical settings
                         include mean-payoff games, where the objective is to
                         optimize the long-run average gain per action, and
                         energy games, where the system has to avoid running
                         out of energy.\par We study \emph{average-energy}
                         games, where the goal is to optimize the long-run
                         average of the accumulated energy. We show that this
                         objective arises naturally in several applications,
                         and that it yields interesting connections with
                         previous concepts in the literature. We prove that
                         deciding the winner in such games is in
                         \textsf{NP}{{\(\cap\)}}\textsf{coNP} and at least as
                         hard as solving mean-payoff games, and we establish
                         that memoryless strategies suffice to win. We also
                         consider the case where the system has to minimize
                         the average-energy while maintaining the accumulated
                         energy within predefined bounds at all times: this
                         corresponds to operating with a finite-capacity
                         storage for energy. We give results for one-player
                         and two-player games, and establish complexity
                         bounds and memory requirements.},
}