Model-Based Diagnosis of Multi-Agent Systems: A Survey

Abstract

As systems involving multiple agents are increasingly deployed, there is a growing need to diagnose failures in such systems. Model-Based Diagnosis (MBD) is a well-known AI technique to diagnose faults in systems. In this approach, a model of the diagnosed system is given, and the real system is observed. A failure is announced when the real system's output contradicts the model's expected output. The model is then used to deduce the defective components that explain the unexpected observation. MBD has been increasingly being deployed in distributed and multi-agent systems. In this survey, we summarize twenty years of research in the field of model-based diagnosis algorithms for MAS diagnosis. We depict three attributes that should be considered when examining MAS diagnosis: (1) The objective of the diagnosis. Either diagnosing faults in the MAS plans or diagnosing coordination faults. (2) Centralized vs. distributed. The diagnosis method could be applied either by a centralized agent or by the agents in a distributed manner. (3) Temporal vs. non-temporal. Temporal diagnosis is used to diagnose the MAS's temporal behaviors, whereas non-temporal diagnosis is used to diagnose the conduct based on a single observation. We survey diverse studies in MBD of MAS based on these attributes, and provide novel research challenges in this field for the AI community.

Structural topic modeling-based systematic review for service robots’ fault diagnosis: content, popularity and regional differences

“…The collaborative work of service robots can significantly enhance the efficiency and quality of tasks. However, intricate deployment configurations and dynamic environments can lead to system-wide failures in scenarios involving multiple agents, potentially resulting in communication barriers and execution conflicts [44]. Distributed fault diagnosis is a suitable choice in MAS.…”

Blame Attribution for Multi-Agent Path Finding Execution Failures

“…Diagnosis of MAS has been studied in various settings w.r.t. different criteria [28]. Attributes like fault types, centralized/decentralized methods of diagnosis, number of observations, and temporality are some of the settings that previous work addresses.…”

“…Others aim to diagnose failures in the cooperation between agents [6,24,27,25,26,23]. We refer to [28] for a comprehensive survey of previous work in this field. In all the mentioned work, the assumption is that agents are either "faulty" or "not faulty", with no intermediary values.…”

“…An important question to ask when a multi-agent system fails is "what is the root cause of the failure?". Previously proposed diagnosis algorithms for MAS [29,28] were designed to answer this ques-tion and localize the responsible faulty events. However, they do not account for the possibility that each agent in the MAS deviates a little bit.…”