Publication

A. Natan, M. Kalech and R. Bartak. Diagnosis of Intermittent Faults in Multi-Agent Systems: An SFL Approach. Artificial Intelligence, 324, 103994, 2023.

Abstract: Multi-Agent Systems (MAS) can be found in a wide variety of applications, including industrial systems, transportation, software systems and more. In such systems, agents may experience faults that affect the performance of the whole system. However, faulty agents might not consistently experience their fault, but rather in certain conditions. For example, a robot with a faulty rotating mechanism will appear healthy if it is tasked to only move in a straight line. Those faults are called Intermittent Faults. Such faults may cause the entire system to fail, but not always. Previous work proposed diagnosis algorithms for MAS, assuming faulty agents persistently behave abnormally. To the best of our knowledge, intermittent faults in MAS have not been concretely explored. In this paper we formally present a novel problem called Diagnosis of Intermittent Faults in Multi-Agent Systems (DIFMAS): a group of agents are observed across multiple runs. In each run, the success/failure of the agents and the system is observed, aiming to explain all the failed runs by diagnosing which agent(s) are faulty. The contributions of this paper are: (1) formalizing DIFMAS as a Model-Based Diagnosis problem, (2) solving it by presenting a Spectrum-Based Fault Localization (SFL) based method, called Multi-Run SFL-based Diagnosis Algorithm (MRSD). Experiments demonstrate that MRSD's outperforms competing SFL-based algorithms. Moreover, the algorithm's performance increases if planned interactions are considered.

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