Monitoring deployed agent teams

Abstract

Recent years are seeing an increasing need for on-line monitoring of deployed distributed teams of cooperating agents, e.g., for visualization, or performance tracking. However, in deployed systems, we often cannot rely on the agents to communicate their state to the monitoring system: (a) we rarely can change the behavior of already-deployed agents to communicate the required information (e.g., in legacy or proprietary systems); (b) different monitoring goals require different information to be communicated (e.g., agents' beliefs vs. plans); and (c) communications may be expensive, unreliable, or insecure. This paper presents a non-intrusive approach based on plan-recognition, in which the monitored agents' state is inferred from observations of their routine actions. In particular, we focus on inference of the team state based on its observed routine communications, exchanged as part of coordinated task execution. The paper includes the following key novel contributions: (i) a linear time probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting general knowledge of teamwork to predict agent responses during normal execution, to reduce monitoring uncertainty; and (iii) a monitoring algorithm that trades expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities, to be represented in a single coherent entity. Our empirical evaluation illustrates that monitoring based on observed routine communications enables significant monitoring accuracy, while not being intrusive. The results also demonstrate a key lesson: A combination of complementary low-quality techniques is cheaper, and better, than a single, highly-optimized monitoring approach.

Execution monitoring in robotics: A survey

“…Since there are many problems involved in the communication between agents [43], their research is focused mainly on the observation of team-members. The key idea is to use various models of social relationships among agents, rather than goal-attentive models of the tasks.…”

Interactive Execution Monitoring of Agent Teams

“…Our EAs recognize physical threats and adversarial activity not expected by the plan, but do not currently perform automated plan or intent recognition on data about adversaries. Both automated plan recognition (Kaminka et al, 2001) and inference of adversarial intent (Franke, Brown, Bell, & Mendenhall, 2000;Bell, Jr., & Brown, 2002) are active areas of research. If algorithms are developed that reliably recognize adversarial plans or intent while using acceptable computational resources, they could easily be invoked within our monitoring framework.…”

Image Retrieval on Internet with Agent

“…There are examples of problems which are appropriate to multi-agent systems research such as modeling social structures [4]. There are also some works that have introduced multi-agent into the execution monitoring of image retrieval processes [5] [6] [7].…”