ITR: Observing, Tracking and Modeling Social Multiagent Systems

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Tracking social agents that have a variety of mental states is quite different from and more challenging than tracking more predictable systems, say aircraft or ships. While multi-target tracking systems typically use physics-based models to track maneuvering vehicles, this work centers on developing probabilistic models of behavior and mental state to address the effects of interactions between agents. This work has practical applications in many areas including, for example: monitoring crowds of people, analyzing urban traffic patterns, and understanding robot and human team behavior. Additionally, this work should accelerate progress in experimental behavioral ecology, especially in the study of social insect systems. For this reason the investigation will initially be conducted in collaboration with biologists in the context of tracking and analyzing the behavior of captive live ant colonies. This research will yield novel, probabilistic algorithms for sensor-based identification, tracking, and modeling of the behavior of social multiagent systems. A tight coupling between tracking and modeling is critical, especially when the observed system is composed of many agents. The focus is on using models of behavior to improve the accuracy of sensor-based tracking, and on using improved tracking to learn better models.