ITR: Observing, Tracking and Modeling Social Multiagent Systems

跟踪具有不同心理状态的社会代理与跟踪更可预测的系统(如飞机或船只)截然不同，也更具挑战性。虽然多目标跟踪系统通常使用基于物理的模型来跟踪机动车辆，但这项工作的中心是开发行为和心理状态的概率模型，以解决代理之间相互作用的影响。这项工作在许多领域都有实际应用，例如：监控人群、分析城市交通模式、理解机器人和人类团队行为。此外，这项工作将加快实验行为生态学的进展，特别是在社会昆虫系统的研究方面。出于这个原因，调查最初将在跟踪和分析圈养的活蚂蚁群体行为的背景下与生物学家合作进行。这项研究将产生新颖的概率算法，用于基于传感器的识别、跟踪和建模社会多代理系统的行为。跟踪和建模之间的紧密耦合是至关重要的，特别是当被观测系统由许多代理组成时。重点是使用行为模型来提高基于传感器的跟踪的准确性，并使用改进的跟踪来学习更好的模型。

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.