Authors:
Charles Zhou
;
Ying Zhao
and
Chetan Kotak
Affiliation:
Quantum Intelligence, Inc., United States
Keyword(s):
Agent learning, Collaboration, Anomaly search, Maritime domain awareness, Intelligence analysis, Unstructured data, Text mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
Abstract:
The Collaborative Learning Agent (CLA) technology is designed to learn patterns from historical Maritime Domain Awareness (MDA) data then use the patterns for identification and validation of anomalies and to determine the reasons behind the anomalies. For example, when a ship is found to be speeding up or slowing down using a traditional sensor-based movement information system such as Automatic Information System (AIS) data, by adding the CLA, one might be able to link the ship or its current position to the contextual patterns in the news, such as an unusual amount of commercial activities; typical weather, terrain and environmental conditions in the region; or areas of interest associated with maritime incidents, casualties, or military exercises. These patterns can help cross-validate warnings and reduce false alarms that come from other sensor-based detections.