Multi-Agent Diagnosis with spatially distributed knowledge

In a large distributed system it is often infeasible or even impossible to maintain a model of the whole system. Instead, several spatially distributed local models of the system have to be used to detect possible faults. Traditional diagnostic tools cannot handle such a set of spatially distributed local models. A Multi-Agent System of diagnostic agents, where each agent has a model of a subsystem, may offer solutions for establishing a global diagnosis of a large distributed system. Unfortunately, any protocol that establishes a global minimal diagnosis, is NP-Hard, even if an agent can determine local minimal diagnoses in polynomial time. This paper presents a protocol that enables agents to determine local minimal diagnoses that are consistent with global diagnoses. Moreover, the protocol ensures that no agent acquires knowledge of global diagnoses. The protocol does not guarantee that a combination of the agents’ local minimal diagnoses is also a global minimal diagnosis. However, for every global minimal diagnosis, there is a combination of local minimal diagnosis.