  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.
