Plan execution monitoring in dynamic and uncertain domains is an
important and difficult problem. Multi-agent environments exacerbate this
problem, given that interacting and coordinated activities of multiple agents
are to be monitored. Previous approaches to this problem do not detect certain
classes of failures, are inflexible, and are hard to scale up. We present a novel
approach, SOCFAD, to failure detection and recovery in multi-agent settings.
SOCFAD is inspired by Social Comparison Theory from social psychology and
includes the following key novel concepts: (a) utilizing other agents in the
environment as information sources for failure detection, (b) a detection and
repair method for previously undetectable failures using abductive inference
based on other agents’ beliefs, and (c) a decision-theoretic approach to
selecting the information acquisition medium. An analysis of SOCFAD is
presented, showing that the new method is complementary to previous
approaches in terms of classes of failures detected.