T.P. Fries and J.H. Graham (USA)
Intelligent agents, multi-agent systems, soft computing, fault diagnosis
Rapid and accurate diagnosis of faults in computer integrated manufacturing systems is necessary in order to prevent expensive downtime. Many artificial intelligence approaches to automated fault diagnosis use either structural or symptom-based reasoning. Functional approaches are unable to provide real-time response due to their computational complexity, whereas, symptom based approaches are only able to handle situations specifically coded in rules. Current hybrid approaches that combine the two methods are too structured in their approach to switching between the reasoning methods and, therefore fail to provide the flexible, rapid response of humans experts. This paper presents a robust, extensible approach to fault diagnosis that allows unstructured switching between reasoning methods using multiple fuzzy intelligent agents that examine the problem domain from a variety of perspectives.
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