CORMS AI: Decision Support System for Monitoring US Maritime Environment

Haleh Vafaie and Carl Cecere

Rule based reasoning and case based reasoning have emerged as two important and complementary reasoning methodologies in artificial intelligence (Al). This paper describes the approach for the development of CORMS AI, a decision support system which employs rule-based and case-based reasoning to assist NOAA’s Center for Operational Oceanographic Products and Services watch standing personnel in monitoring the quality of marine environmental data and information.

CORMS AI has been in operation since July 2003. The system accurately and reliably identifies suspect data and network disruptions, and has decreased the amount of time it takes to identify and troubleshoot sensor, network, and server failures. CORMS AI has proven to be robust, extendable, and cost effective. It is estimated that CORMS AI will save government over one million dollars per year when its full range of quality control monitoring capabilities is implemented.

Subjects: 1.5 Diagnosis; 16. Real-Time Systems

Submitted: Apr 14, 2005

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