论文标题
游戏生成网络框架及其应用于关系推理
A Game Generative Network Framework with its Application to Relationship Inference
论文作者
论文摘要
游戏过程是一个系统，一个代理的决策可以影响其他代理的决策。在现实世界中，代理商之间的社会影响力和关系可能会影响游戏行为的决策。反过来，这也使我们有机会通过在游戏过程中观察到的相互作用来挖掘代理商的信息。在本文中，我们提出了一个游戏生成网络（GGN）框架，该框架利用了真实的游戏结果和理想的游戏模型之间的偏差来生成游戏过程的网络，这为通过网络挖掘方法提供了一扇门，可以通过网络挖掘方法来理解游戏行为。我们说明了如何应用GGN来推断代理商与游戏行为之间的隐藏关系，并在团队游戏上进行实验，这是一个具体的示例。实验结果表明，我们提出的框架可以揭示这种游戏中代理商的隐藏关系。
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 opportunity to mine such information from agents by the observed interactions of them in a game process. In this paper, we propose a Game Generative Network (GGN) framework that utilizes the deviation between the real game outcome and the ideal game model to generate networks for game processes, which opens a door for understanding agents with game behaviors by network mining approaches. We illustrate how to apply GGNs to infer the hidden relationships between agents with game behaviors and conduct experiments on team games as a concrete example. Experimental results demonstrate that our proposed framework can reveal the hidden relationships of agents in such games.
