I'm OK, you're OK, we're OK: Experiments in distributed and centralized socially attentive monitoring

Execution monitoring is a critical challenge for agents in dynamic, complex, multi-agent domains. Existing approaches utilize goal-attentive models which monitor achievement of task goals. However, they lack knowledge of the intended relationships which should hold among the agents, and so fail to address key opportunities and difficulties in multi-agent settings. We explore SAM, a novel complementary framework for social monitoring that utilizes knowledge of social relationships among agents in monitoring them. We compare the performance of SAM when monitoring is done by a single agent in a centralized fashion, versus team monitoring in a distributed fashion. We experiment with several SAM instantiations, algorithms that are sound and incomplete, unsound and complete, and both sound and complete. While a more complex algorithm appears useful in the centralized case (but is unsound), the surprising result is that a much simpler algorithm in the distributed case is both sound and complete. We present a set of techniques for practical, efficient implementations with rigorously proven performance guarantees, and systematic empirical validation.