arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0706.2619cs.LOcs.GT
收藏

不完全信息Ω正则对策的算法

Algorithms for Omega-Regular Games with Imperfect Information

Krishnendu Chatterjee, Laurent Doyen, Thomas A. Henzinger, <br> Jean-Francois Raskin

摘要:

我们研究了基于观察的策略,两个球员回合制游戏的图与欧米茄定期目标。基于观察的策略依赖于关于戏剧历史的不完全信息,即过去的观察序列。这样的游戏发生在一个控制器的合成中,该控制器看不到植物的私有状态。我们的主要结果是双重的。首先,我们给出了一个不动点算法,用于计算一组状态,从该状态中,玩家可以用确定性的基于观察的策略来赢得任何ω-正则目标。不动点是在状态集的反链格中计算的。该算法具有目标导向和避免在博弈图上构造显式子集的优点。其次,我们给出了一个算法,用于计算一个球员可以赢得概率为1的随机观察为基础的战略Buechi目标的状态集。这个集合很有趣,因为在没有完美信息的情况下,随机策略比确定性策略更强大。我们证明了我们的算法是最佳的匹配下界。

英文摘要:

We study observation-based strategies for two-player turn-based games on graphs with omega-regular objectives. An observation-based strategy relies on imperfect information about the history of a play, namely, on the past sequence of observations. Such games occur in the synthesis of a controller that does not see the private state of the plant. Our main results are twofold. First, we give a fixed-point algorithm for computing the set of states from which a player can win with a deterministic observation-based strategy for any omega-regular objective. The fixed point is computed in the lattice of antichains of state sets. This algorithm has the advantages of being directed by the objective and of avoiding an explicit subset construction on the game graph. Second, we give an algorithm for computing the set of states from which a player can win with probability 1 with a randomized observation-based strategy for a Buechi objective. This set is of interest because in the absence of perfect information, randomized strategies are more powerful than deterministic ones. We show that our algorithms are optimal by proving matching lower bounds.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑