Abstract
AbstractThis paper presents a MAS dedicated to abnormal behaviors detection and alerts triggering in the maritime surveillance area. This MAS uses anomalies issued from an experienced Rule Engine implementing maritime regulation. It evaluates ships behavior cumulating the importance of related anomalies and triggers relevant alerts towards human operators involved in maritime surveillance. These human operators evaluate triggered alerts and confirm or invalidate them. Invalidated alerts are sent back to the MAS for a learning step since it self-adapts anomalies values to be consistent with human operators feedbacks. This MAS is implemented in the context of the project I2C, an EU funded project dedicated to abnormal ships behavior detection and early identification of threats such as oil slick, illegal fishing, or lucrative criminal activities (e.g. goods, drugs, or weapons smuggling).KeywordsMaritime SurveillanceAlertLearningAdaptive MAS
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