论文标题
关系事件模型与逆增强学习之间的连接，以表征小组交互序列
Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences
论文作者
论文摘要
在本文中，我们探讨了从网络科学领域与逆增强学习（IRL）之间的关系事件模型（REM）之间的以前未知的连接，从机器学习领域来表征在小组设置中表征有向社交互动事件的序列的能力。 REM是解决此类问题的常规方法，而IRL的应用主要是一条不败的道路。我们首先检查REM和IRL的数学组成部分，并在两种方法之间找到直接的类比以及IRL方法的独特特征。我们证明了IRL的特殊实用性，可以通过经验实验来表征团体社交互动，在该实验中，我们利用IRL根据一系列虚拟现实游戏玩家的互动和合作以实现共同目标的序列来推断个人行为偏好。我们的比较和实验介绍了社交行为分析的新观点，并有助于激发社交网络分析和机器学习联系的新研究机会。
In this paper we explore previously unidentified connections between relational event model (REM) from the field of network science and inverse reinforcement learning (IRL) from the field of machine learning with respect to their ability to characterize sequences of directed social interaction events in group settings. REM is a conventional approach to tackle such a problem whereas the application of IRL is a largely unbeaten path. We begin by examining the mathematical components of both REM and IRL and find straightforward analogies between the two methods as well as unique characteristics of the IRL approach. We demonstrate the special utility of IRL in characterizing group social interactions with an empirical experiment, in which we use IRL to infer individual behavioral preferences based on a sequence of directed communication events from a group of virtual-reality game players interacting and cooperating to accomplish a shared goal. Our comparison and experiment introduce fresh perspectives for social behavior analytics and help inspire new research opportunities at the nexus of social network analysis and machine learning.
