Army logisticians and fleet managers need an effective method to extract actionable information from their large data stores. They need to extract root cause and case-based analysis to diagnose or predict breakdowns. An Enterprise Intelligence System that extracts data from multiple sources, fuses that information, and disseminates the results in the form of products on the readiness of fleet vehicles would address this challenge. 21st Century Systems, Inc., teamed with the Missouri University of Science and Technology, proudly proposes to continue research and development of the Agent-Enabled Logistics Enterprise Intelligence System (AELEIS) to meet this challenge. AELEIS features an agent-based approach to autonomously mine data from multiple sources and combine the information into actionable knowledge. AELEIS processes data in many forms (e.g., reports, data logs, database entries, etc.), including textual data. The processing occurs in the background so information is available at a momentâs notice when the operator needs it. AELEIS uses advanced technologies, such as Adaptive Resonance Theory, Evidential Data Reasoning, and Mahalanobis-Taguchi System, for diagnostics and prognostics. This combination results in a tool that will allow logistics operators, managers, and commanders to make faster, better decisions that will ultimately save lives on the battlefield.
Keywords: Expert Systems, Machine Learning, Knowledge Based Systems, Vehicle Health Monitoring, Data Reasoning, Intelligent Agent Software, Mahalanobis-Taguchi System, Data Mining And C
