论文标题
人类行为的多时间尺度建模
Multi-Timescale Modeling of Human Behavior
论文作者
论文摘要
近年来，人工智能（AI）代理商的作用已经从成为基本工具到与人类一起工作的社会智能代理人朝着共同的目标发展。在这种情况下，在AI代理中，人们非常需要通过观察人类队友的过去行动来预测未来行为的能力。面向目标的人类行为是复杂的，分层的，并且在多个时间尺度上展开。尽管有这样的观察，但对使用多时间尺度特征来建模这种行为的关注很少。在本文中，我们提出了一个LSTM网络体系结构，该体系结构在多个时间范围内处理行为信息以预测未来的行为。我们证明，与未在多个时间尺度上建模行为的方法相比，多个时间尺度上建模行为的方法可大大改善对未来行为的预测。我们对在基于虚拟Minecraft的测试台模拟的城市搜索和救援方案中收集的数据进行评估，并将其性能与许多有效基线的效果以及其他未在多个时间尺度处理输入的方法。
In recent years, the role of artificially intelligent (AI) agents has evolved from being basic tools to socially intelligent agents working alongside humans towards common goals. In such scenarios, the ability to predict future behavior by observing past actions of their human teammates is highly desirable in an AI agent. Goal-oriented human behavior is complex, hierarchical, and unfolds across multiple timescales. Despite this observation, relatively little attention has been paid towards using multi-timescale features to model such behavior. In this paper, we propose an LSTM network architecture that processes behavioral information at multiple timescales to predict future behavior. We demonstrate that our approach for modeling behavior in multiple timescales substantially improves prediction of future behavior compared to methods that do not model behavior at multiple timescales. We evaluate our architecture on data collected in an urban search and rescue scenario simulated in a virtual Minecraft-based testbed, and compare its performance to that of a number of valid baselines as well as other methods that do not process inputs at multiple timescales.
