Exception diagnosis in agent-based grid computing

Diagnosing exceptions in multi-agent systems (MAS) is a complex task due to the distributed nature of the data and control in such systems. This complexity is exacerbated in open environments where independently developed autonomous agents interact with each other in order to achieve their goals. Inevitably, exceptions would occur in such MAS and these exceptions can arise at one of three levels, namely environmental, knowledge and social levels. In this paper we propose a novel exception diagnosis system that is able to analyse and detect exceptions effectively. The proposed architecture consists of specialised exception diagnosis agents called sentinel agents. The sentinel agents are equipped with knowledge of observable abnormal situations, their underlying causes, and resolution strategies associated with these causes. The sentinel agent applies a heuristic classification approach to collect related data from affected agents in order to uncover the underlying causes of the observed symptoms. We illustrate and evaluate our proposed architecture using an agent-based grid computing case study.