Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach (1106.1816v1)

Abstract: Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' routine communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade 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. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.

• The paper proposes a non-intrusive method for monitoring distributed multi-agent teams by applying plan recognition to communication exchanges.
• The approach utilizes probabilistic plan recognition and coherence heuristics, empirically demonstrating improved monitoring accuracy in complex simulated team scenarios.
• Scalability challenges in large systems are addressed by the YOYO* algorithm, making the method applicable to real-world multi-agent applications in logistics and defense.

Overhearing in Team Monitoring: A Multi-Agent Plan-Recognition Approach

The article presents a thorough investigation into the complexities and feasible solutions for monitoring multi-agent systems, particularly distributed teams where direct observation is impractical. Traditional methods, like report-based monitoring, requiring agents to constantly communicate their state, face limitations due to their intrusiveness, bandwidth demands, and susceptibility to communication losses. This work advances an innovative non-intrusive method for team monitoring known as "overhearing," employing a sophisticated plan-recognition framework that interprets team states from observed communication exchanges among agents.

Central Contributions

The authors delineate several key methodological advancements that address the pertinent challenges, such as computational demands, observation scarcity, and scalability issues:

1. Probabilistic Plan-Recognition Algorithm: This algorithm efficiently processes communications, inferring unobservable states from observable actions. It leverages a structured probabilistic model, where plan states are represented as boolean variables, facilitating a quantitative assessment of various hypotheses.
2. Socially-Attentive Monitoring: The method exploits social structures and inter-agent relationships, utilizing coherence heuristics to not only optimize the computational load but also improve hypothesis accuracy. The assumption is that agents, as part of a coherent unit, engage in anticipated joint actions and communications, enabling more precise inferences.
3. YOYO* Algorithm: To adapt to large-scale systems, the YOYO* algorithm represents multi-agent team activity as a single coherent entity. It trades expressivity (the ability to accurately monitor coordination failures) for scalability, simplifying the complexity from being dependent on the number of team members to a dependency on the structure and hierarchy within teams.

Empirical Evaluation

The effectiveness of these approaches was evaluated through empirical studies in a dynamic, distributed team scenario involving complex task execution, such as simulated evacuations. The results indicated significant improvements in monitoring accuracy, reaching levels comparable to human experts. Strategically combining coherence with temporal order models and expected communication patterns enhanced prediction efficacy despite incomplete observation data.

Implications and Prospects for Future AI Work

The implications of this research span both practical applications and theoretical advancements in AI monitoring systems. The practical solutions described here are particularly beneficial for multi-agent systems embedded in logistics, defense, and collaborative operations, providing robust tools for real-time monitoring without necessitating invasive changes to agent behavior.

Theoretically, these methodologies present intriguing implications for AI development, particularly concerning the representation and reasoning about team-based activities. Understanding and predicting multi-agent dynamics through coherent plan recognition offer fertile ground for further research into scalable AI systems capable of adaptive, reliable behavior across domains with communication and observation constraints.

Exploring learning-based augmentations to improve prediction accuracy under uncertain and partially observable scenarios is a viable direction for future work. Additionally, further refining the balance between expressivity and scalability in plan-recognition systems could unlock more sophisticated applications, potentially extending beyond homogenous agent teams to heterogeneous and human-in-the-loop systems.

In summary, this research lays a foundational framework for non-intrusive, robust monitoring of complex agent teams through communicated actions, a vital contribution as multi-agent systems expand into more diverse and demanding environments.