Unsupervised Role Discovery Using Temporal Observations of Agents
adaptive agents and multi-agents systems(2019)
摘要
Agent-based modeling of multi-agent systems has enormous potential with applications in modeling social, economic, medical and other application domains containing temporal data. We propose an unsupervised approach to discovering common roles by observing agents over time, allowing us to construct a role-based representation of multi-agent systems that aids in understanding and interpreting the state of the system. We validate our approach on both a soccer and a StarCraft dataset, and show that unsupervised role discovery through observation can provide meaningful insight into the state of a multi-agent system, aiding or even replacing game state data for interpretation or understanding of the system.
更多查看译文
关键词
Multi-agent systems,Unsupervised learning,Temporal learning,Interpretability
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络