When studying extensive-form games it is commonly assumed that players make their decisions individually. One usually does not allow the possibility for the players to negotiate their respective strategies and formally commit themselves to future moves. As a consequence, many non-zero-sum games have been shown to have equilibrium outcomes that are suboptimal and arguably counter-intuitive. For this reason we feel there is a need to explore a new line of research in which game-playing agents are allowed to negotiate binding agreements before they make their moves. We analyze what happens under such assumptions and define a new equilibrium solution concept to capture this. We show that this new solution concept indeed yields solutions that are more efficient and, in a sense, closer to what one would expect in the real world. Furthermore, we demonstrate that our ideas are not only theoretical in nature, but can also be implemented on bounded rational agents, with a number of experiments conducted with a new algorithm that combines techniques from Automated Negotiations, (Algorithmic) Game Theory, and General Game Playing. Our algorithm, which we call Monte Carlo Negotiation Search, is an adaptation of Monte Carlo Tree Search that equips the agent with the ability to negotiate. It is completely domain-independent in the sense that it is not tailored to any specific game. It can be applied to any non-zero-sum game, provided that its rules are described in Game Description Language. We show with several experiments that it strongly outperforms non-negotiating players, and that it closely approximates the theoretically optimal outcomes, as defined by our new solution concept.

中文翻译：

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大型游戏的战略谈判

在研究广泛形式的游戏时，通常假定玩家分别做出决定。通常不允许玩家谈判各自的策略并正式致力于未来的行动。结果，许多非零和博弈已显示具有次优的平衡结果，并且可以说是违反直觉的。因此，我们认为有必要探索一条新的研究领域，即允许游戏玩家在采取行动之前就具有约束力的协议进行谈判。我们分析了在这种假设下会发生什么，并定义了一个新的均衡解决方案概念来捕捉这一点。我们表明，这种新的解决方案概念确实可以提供更有效的解决方案，从某种意义上讲，它更接近于现实世界中的期望。此外，我们证明了我们的想法不仅是理论上的，而且还可以在有界理性主体上实施，并且使用结合了自动协商，（算法）博弈论和常规博弈技术的新算法进行了许多实验。我们的算法（称为蒙特卡洛协商搜索）是对蒙特卡洛树搜索的一种改编，它使代理具有协商的能力。它不是针对任何特定游戏量身定制的，因此完全独立于域。可以将其应用于任何非零和游戏，只要其规则以游戏描述语言描述即可。我们通过几个实验表明，它的表现大大优于非谈判参与者，并且与我们的新解决方案概念所定义的理论上最佳的结果非常接近。