Even though the scientific community has been struggling, for some time now, to build human-like systems, which act as experts in some area, this quest is still being undertaken and some human abilities are yet to implement in artificial experts. The most common human functions such as commonsense, temporal and non-monotonic reasoning have not yet been mapped in developed systems, even though some theoretical breakthroughs have already been accomplished. This was mainly due to the inherent computational complexity of the theoretical approaches. In the particular area of fault diagnosis in power systems, however, some systems which tried to solve the problem, have been deployed using methodologies such as production rule based expert systems, neural networks, fuzzy expert systems, etc. SPARSE was one of the developed systems and, in the sequence of its development came the need to cope with incomplete or incorrect information as well as the traditional problems for power systems fault diagnosis based on SCADA information retrieval (alarms). This paper presents an architecture for a decision support system, which can perform diagnosis on SCADA alarms, insuring soft real-time operation with incomplete, incorrect or domain incoherent information handling ability.
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