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arXiv 2407.06288cs.GT
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带充能的竞价博弈

Bidding Games with Charging

Guy Avni, Ehsan Kafshdar Goharshady, Thomas A. Henzinger, Kaushik Mallik

AI总结:

该研究提出带充能的竞价博弈模型,证明其仍存在顶点阈值比例,指出不动点非唯一带来的复杂度问题,给出Rabin和Streett目标的首个复杂度下界,并解决了安全与可达性博弈的充能分配修复问题。

摘要:

图博弈是计算机科学中众多自动化设计问题的算法核心。这类博弈通常由两名玩家在给定图上进行,玩家按照预设规则沿边持续移动一枚令牌,胜负由令牌从给定初始位置出发遍历的无限路径决定。在竞价博弈中,玩家初始拥有一定货币预算,需通过竞价获得每一步的令牌移动权。每一轮竞价都会影响玩家的可用预算,这是预算唯一的更新形式。\n我们提出带充能的竞价博弈,玩家在博弈过程中还可通过收集与顶点相关的充能值来提升自身预算。与所有充能值均为零的传统竞价博弈不同,带充能的竞价博弈可产生非平凡的循环行为。我们证明传统竞价博弈的核心性质可推广至带充能的竞价博弈:每个顶点都存在一个阈值比例,即玩家从该顶点出发赢得博弈所需总预算的充要比例。传统竞价博弈的阈值对应线性方程组的唯一不动点,而在带充能的博弈中,这些不动点不再唯一,这大幅增加了无限时长目标阈值的存在性证明与算法计算的复杂度。\n我们还给出了Rabin目标和Streett目标下阈值计算的复杂度下界,这是所有形式竞价博弈(无论是否带充能)中已知的首个下界;此外,我们解决了目标不可满足的安全性与可达性博弈的如下修复问题:能否将给定数量的充能值分配给玩家,使得博弈目标得以满足?

英文摘要:

Graph games lie at the algorithmic core of many automated design problems in computer science. These are games usually played between two players on a given graph, where the players keep moving a token along the edges according to pre-determined rules, and the winner is decided based on the infinite path traversed by the token from a given initial position. In bidding games, the players initially get some monetary budgets which they need to use to bid for the privilege of moving the token at each step. Each round of bidding affects the players' available budgets, which is the only form of update that the budgets experience. We introduce bidding games with charging where the players can additionally improve their budgets during the game by collecting vertex-dependent charges. Unlike traditional bidding games (where all charges are zero), bidding games with charging allow non-trivial recurrent behaviors. We show that the central property of traditional bidding games generalizes to bidding games with charging: For each vertex there exists a threshold ratio, which is the necessary and sufficient fraction of the total budget for winning the game from that vertex. While the thresholds of traditional bidding games correspond to unique fixed points of linear systems of equations, in games with charging, these fixed points are no longer unique. This significantly complicates the proof of existence and the algorithmic computation of thresholds for infinite-duration objectives. We also provide the lower complexity bounds for computing thresholds for Rabin and Streett objectives, which are the first known lower bounds in any form of bidding games (with or without charging), and we solve the following repair problem for safety and reachability games that have unsatisfiable objectives: Can we distribute a given amount of charge to the players in a way such that the objective can be satisfied?

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