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Gal A. Kaminka, David V. Pynadath,
and Milind Tambe. Monitoring Teams by Overhearing: A Multi-Agent Plan Recognition
Approach. Journal of Artificial Intelligence Research, 17:83–135, 2002.
[PDF]687.2kB [gzipped postscript]257.6kB
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 teammembers' 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 at capable of monitoring at human-expert levels, despite the difficulty of the task.
@Article{jair02,
author = {Gal A. Kaminka and David V. Pynadath and Milind Tambe},
title = {Monitoring Teams by Overhearing: A Multi-Agent Plan Recognition Approach},
journal = {Journal of Artificial Intelligence Research},
year = {2002},
volume = {17},
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 teammembers' 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 at capable of monitoring at human-expert levels, despite the difficulty of the task.
},
wwwnote = {},
pages = {83--135},
}
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