\emph{Ex ante} correlation is becoming the mainstream approach for \emph{sequential adversarial team games}, where a team of players faces another team in a zero-sum game. It is known that team members' asymmetric information makes both equilibrium computation \textsf{APX}-hard and team's strategies not directly representable on the game tree. This latter issue prevents the adoption of successful tools for huge 2-player zero-sum games such as, \emph{e.g.}, abstractions, no-regret learning, and subgame solving. This work shows that we can recover from this weakness by bridging the gap between sequential adversarial team games and 2-player games. In particular, we propose a new, suitable game representation that we call \emph{team-public-information}, in which a team is represented as a single coordinator who only knows information common to the whole team and prescribes to each member an action for any possible private state. The resulting representation is highly \emph{explainable}, being a 2-player tree in which the team's strategies are behavioral with a direct interpretation and more expressive than the original extensive form when designing abstractions. Furthermore, we prove payoff equivalence of our representation, and we provide techniques that, starting directly from the extensive form, generate dramatically more compact representations without information loss. Finally, we experimentally evaluate our techniques when applied to a standard testbed, comparing their performance with the current state of the art.
翻译:\ emph{ Ex ate} 相关关系正在成为 \ emph{ 序列对抗性球队游戏的主流方法 。 一个球员团队在零和游戏中面对另一个球队。 众所周知, 球员的不对称信息使得在游戏树上无法直接代表均衡计算\ textsf{ APX} 硬和球队的战略。 后一个问题阻碍为大型的 2 球员零和游戏, 如 \ emph{ e.} 、 抽象化、 不累进学习和 子球队的游戏采用成功的工具。 这项工作表明, 我们可以通过弥合连续的对抗性球队游戏和 2 球队游戏之间的差距来从这一弱点中恢复过来。 特别是, 我们建议一个新的合适的游戏代表, 我们称之为\ emph{team- public- info} 。 团队作为单一的协调员, 他只知道整个球队共有的信息, 并且为每个成员提供可能的私人国家的行动。 由此导致的对比是高度的 emph{ 解释 和 亚化 。 。 作为开始的2 直观的2 的游戏的游戏代表, 我们直接的游戏代表, 提供我们团队的 直接的游戏的游戏的模拟的游戏的模型的模型, 提供 直接的模型的模型的演示 。