A game process is a system where the decisions of one agent can influence the decisions of other agents. In the real world, social influences and relationships between agents may influence the decision makings of agents with game behaviors. And in turn, this also gives us the possibility to mine some information from such agents, such as the relationships between them, by the interactions in a game process. In this paper, we propose a Game Generative Network (GGN) framework which utilizes the deviation between the real game outcome and the ideal game model to build networks for game processes, which opens a door for understanding more about agents with game behaviors by graph mining approaches. We apply GGN to the team game as a concrete application and conduct experiments on relationship inference tasks.

中文翻译：

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一种游戏生成网络框架及其在关系推理中的应用

博弈过程是一个系统，其中一个代理的决定可以影响其他代理的决定。在现实世界中，智能体之间的社会影响和关系可能会影响具有游戏行为的智能体的决策。反过来，这也使我们有可能通过游戏过程中的交互从这些代理中挖掘一些信息，例如它们之间的关系。在本文中，我们提出了一种游戏生成网络 (GGN) 框架，该框架利用真实游戏结果与理想游戏模型之间的偏差来构建游戏过程网络，这为通过图挖掘了解更多具有游戏行为的代理打开了一扇门方法。我们将 GGN 应用到团队游戏中作为具体应用，并对关系推理任务进行实验。