Diagnosis as an integral part of multi-agent adaptability

Agents working under real world conditions may face an environment capable of changing rapidly from one moment to the next, either through perceived faults, unexpected interactions or adversarial intrusions. The members of a multi-agent system can gracefully and efficiently handle such situations by adapting, either by evolving internal structures and behavior or repairing or isolating those external influences believed to be malfunctioning. The first step in achieving adaptability is diagnosis-being able to accurately detect and determine the cause of a fault based on its symptoms. In this paper we examine how domain independent diagnosis plays a role in multi-agent systems, including the information required to support and produce diagnoses. Particular attention is paid to coordination based diagnosis directed by a causal model. Several examples are described in the context of an Intelligent Home environment, and the issue of diagnostic sensitivity versus efficiency is addressed.