Privacy-aware Distributed Diagnosis of Multi-Agent Plans

In many multi-agent systems (MAS), agents are assigned to perform tasks. These tasks require designing plans for the agents, which are called multi-agent plan (MAP). Such systems are prone to failures due to variety of reasons. When a failure occurs, there is a need to diagnose it and identify the faulty agents. This problem has been traditionally addressed in a centralized manner. However, in some systems, agents might not be able to share plans due to privacy or single point of failure reasons. To address this challenge, we propose Distributed Diagnosis of Multi-Agent Plans (DDMAP) algorithms, which identify faulty agents of a MAS in a distributed manner without sharing plans. The contributions of this paper are: (1) formalizing DDMAP as a model-based diagnosis problem, and (2) presenting synchronous, asynchronous and semi-asynchronous distributed algorithms to diagnose the faulty agents. Experiments show that the semi-asynchronous algorithm performs better in terms of run-time while the performance in terms of communication overhead is comparable.