Diagnosis of single and multi-agent plans

We discuss the application of Model Based Diagnosis in agent-based planning. We model a plan as a system to be diagnosed and assume that agents can monitor the execution of the plan by making partial observations during plan execution. These observations are used by the agents to explain plan deviations (errors) by qualifying some action instances as behaving abnormally. We prefer those qualifications that are maximum informative, i.e. explain as much as possible. Since in a plan several instances of the same action might occur, an error occurring in one instance might be used to predict the occurrence of the same error in an action instance to be executed later on. To account for such correlations, we introduce causal rules to generate diagnoses from action instances qualified as abnormally and we introduce Pareto minimal causal diagnoses as the right extension of classical minimal diagnoses.Next, we consider the multi-agent perspective where each agent is responsible for a part of the total plan, we show how plan-diagnoses of these partial plans are related to diagnoses of the total plan and how global diagnoses can be obtained in a distributed way.