Execution Monitoring and Diagnosis in Multi-Agent Environments

Abstract

Agents in complex, dynamic, multi-agent environments face uncertainty in the execution of their tasks, as their sensors, plans, and actions may fail unexpectedly, e.g., the weather may render a robot's camera useless, its grip too slippery, etc. The explosive number of states in such environments prohibits any resource-bounded designer from predicting all failures at design time. This situation is exacerbated in multi-agent settings, where interactions between agents increase the complexity. For instance, it is difficult to predict an opponent's behavior. Agents in such environments must therefore rely on runtime execution monitoring and diagnosis to detect a failure, diagnose it, and recover. Previous approaches have focused on supplying the agent with goal-attentive knowledge of the ideal behavior expected of the agent with respect to its