Looking at Mean-Payoff through Foggy Windows
Abstract
Mean-payoff games (MPGs) are infinite duration two-player zero-sum games played on weighted graphs. Under the hypothesis of perfect information, they admit memoryless optimal strategies for both players and can be solved in NP-intersect-coNP. MPGs are suitable quantitative models for open reactive systems. However, in this context the assumption of perfect information is not always realistic. For the partial-observation case, the problem that asks if the first player has an observation-based winning strategy that enforces a given threshold on the mean-payoff, is undecidable. In this paper, we study the window mean-payoff objectives that were introduced recently as an alternative to the classical mean-payoff objectives. We show that, in sharp contrast to the classical mean-payoff objectives, some of the window mean-payoff objectives are decidable in games with partial-observation.
Keywords
Cite
@article{arxiv.1504.02947,
title = {Looking at Mean-Payoff through Foggy Windows},
author = {Paul Hunter and Guillermo A. Pérez and Jean-François Raskin},
journal= {arXiv preprint arXiv:1504.02947},
year = {2015}
}