THE COLLABORATIVE LEARNING AGENT (CLA) IN TRIDENT WARRIOR 08 EXERCISE

Charles Zhou, Ying Zhao, Chetan Kotak

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.

References

  1. Bonifacio, M., et al., 2002. A peer-to-peer architecture for distributed knowledge management. in the 3rd International Symposium on Multi-Agent Systems, Large Complex Systems, and E-Businesses.
  2. Brin, S. and Page, L.,1998. The Anatomy of a Large-scale Hypertextual Web Search engine.” In Proceedings of the Seventh International World Wide Web Conference.
  3. Foltz, P.W., 2002. Quantitative Cognitive Models of Text and Discourse Processing. In The Handbook of Discourse Processes. Mahwah, NJ: Lawrence Erlbaum Publishing,
  4. Gerber, C., 2005. Smart Searching, New technology is helping defense intelligence analysts sort through huge volumes of data. In Military Information Technology, 9(9).
  5. Hoff, P.D., Raftery, A.E. and Handcock, M.S., 2002. Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association, 97. http://elvis.slis.indiana.edu/fetched/article/1129.htm
  6. Tecuci G., Boicu M., 2008. A Guide for Ontology Development with Disciple, Research Report 3, Learning Agents Center, George Mason University.
  7. Tecuci G., Boicu M., Marcu D., Boicu C., Barbulescu M., Ayers C., Cammons D., 2007. Cognitive Assistants for Analysts, in John Auger, William Wimbish (eds.), Proteus Futures Digest: A Compilation of Selected Works Derived from the 2006 Proteus Workshop, Joint publication of the National Intelligence University, Office of the Director of National Intelligence, and US Army War College Center for Strategic Leadership.
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Paper Citation


in Harvard Style

Zhou C., Zhao Y. and Kotak C. (2009). THE COLLABORATIVE LEARNING AGENT (CLA) IN TRIDENT WARRIOR 08 EXERCISE . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 323-328. DOI: 10.5220/0002332903230328


in Bibtex Style

@conference{kdir09,
author={Charles Zhou and Ying Zhao and Chetan Kotak},
title={THE COLLABORATIVE LEARNING AGENT (CLA) IN TRIDENT WARRIOR 08 EXERCISE},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={323-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002332903230328},
isbn={978-989-674-011-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - THE COLLABORATIVE LEARNING AGENT (CLA) IN TRIDENT WARRIOR 08 EXERCISE
SN - 978-989-674-011-5
AU - Zhou C.
AU - Zhao Y.
AU - Kotak C.
PY - 2009
SP - 323
EP - 328
DO - 10.5220/0002332903230328