Multi-Timescale Modeling of Human Behavior

TL;DR

This paper introduces a multi-timescale LSTM model to predict goal-oriented human behavior more accurately by capturing hierarchical actions across different temporal scales, demonstrated in a virtual rescue scenario.

Contribution

The paper presents a novel multi-timescale LSTM architecture for modeling complex human behavior, significantly improving prediction accuracy over existing single-timescale methods.

Findings

Multi-timescale modeling outperforms single-timescale approaches.

The approach effectively captures hierarchical behavior patterns.

Improved prediction accuracy in simulated rescue scenario.

Abstract

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.