The window mechanism was introduced by Chatterjee et al. to reinforce mean-payoff and total-payoff objectives with time bounds in two-player turn-based games on graphs. It has since proved useful in a variety of settings, including parity objectives in games and both mean-payoff and parity objectives in Markov decision processes. We study window parity objectives in timed automata and timed games: given a bound on the window size, a path satisfies such an objective if, in all states along the path, we see a sufficiently small window in which the smallest priority is even. We show that checking that all time-divergent paths of a timed automaton satisfy such a window parity objective can be done in polynomial space, and that the corresponding timed games can be solved in exponential time. This matches the complexity class of timed parity games, while adding the ability to reason about time bounds. We also consider multi-dimensional objectives and show that the complexity class does not increase. To the best of our knowledge, this is the first study of the window mechanism in a real-time setting.
翻译:由 Chatterjee et al. 推出的窗口机制是为了在图形上的双人翻转游戏中,加强平均回报和总回报目标,同时在两个玩家翻转游戏中设定时限。此后,该窗口机制在各种环境中被证明是有用的,包括游戏中的对等目标以及Markov 决策程序中的对等和对等目标。我们在时间自动自动游戏和定时游戏中研究了窗口对等目标:在窗口大小上设定一个界限,如果在这条道路上的各州,我们看到一个足够小的窗口,其最不重要的优先位置是偶数的,那么这条路径就达到了这样一个目标。我们发现,一个定时的自动映射器能够满足这种窗口对等目标,而相应的定时游戏可以在指数时间内解决。这与时间对时间对等游戏的复杂分类相匹配,同时增加时间界限解释的能力。 我们还考虑到多维目标,并表明复杂级别不会增加。 据我们所知,这是实时设置窗口机制的首项研究。