Towards dynamic tracking of multi-agents teams: An initial report

This paper takes first steps to address the challenge of plan recognition for dynamic multi-agents teams, in the context of suspicious behavior recognition. Plan recognition is the process of inferring other agents' plans and goals based on their observable actions. Team plan recognition poses the challenge of such inference, of a team's joint goals and plans. Most previous work have focused on recognizing specific (and limited) coordinated behaviors and do not deal with the problem of identifying interactions between groups of agents, and with identifying suspicious behavior from this information. In contrast, this paper utilizes the information from group of agents, to identify the interactions between groups of agents, using a Dynamic Hierarchical Group Model (DHGM) that tracks the dynamic grouping and ungrouping of agents. We show how such information can be used to identify potential suspicious behavior. These suspicious behaviors can be captured only when tracking individuals with respect to the group and not as individuals. For example, identifying passenger in the airport that behaves differently from other passengers in the same group. While reasoning about individual agents in a multi-agents framework is expensive, we reduce this complexity by utilizing the DHGM that encapsulate shared data of agents in the same group. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.