When performing complex tasks, humans naturally reason at multiple temporal and spatial resolutions simultaneously. We contend that for an artificially intelligent agent to effectively model human teammates, i.e., demonstrate computational theory of mind (ToM), it should do the same. In this paper, we present an approach for integrating high and low-resolution spatial and temporal information to predict human behavior in real time and evaluate it on data collected from human subjects performing simulated urban search and rescue (USAR) missions in a Minecraft-based environment. Our model composes neural networks for high and low-resolution feature extraction with a neural network for behavior prediction, with all three networks trained simultaneously. The high-resolution extractor encodes dynamically changing goals robustly by taking as input the Manhattan distance difference between the humans' Minecraft avatars and candidate goals in the environment for the latest few actions, computed from a high-resolution gridworld representation. In contrast, the low-resolution extractor encodes participants' historical behavior using a historical state matrix computed from a low-resolution graph representation. Through supervised learning, our model acquires a robust prior for human behavior prediction, and can effectively deal with long-term observations. Our experimental results demonstrate that our method significantly improves prediction accuracy compared to approaches that only use high-resolution information.

本文研究如何让人工智能在多个时间和空间分辨率上有效地对人类队友进行建模，并针对在Minecraft环境中执行任务的实验结果，展示了一种利用高低分辨率空间和时间信息提高人类行为预测准确性的方法。

使用多个时间和空间分辨率的特征实时预测人类行为