Knowledge Acquisition for Adversary Course of Action Prediction Models

AAAI Fall Symposium - Technical Report

Volumn FS-02-05, Issue , 2002, Pages

  Brown, Scott M ^a   Santos, Eugene ^b   Bell, Benjamin ^c  

^a Information Operations Program Office   (United States)

^b University of Connecticut   (United States)

^c CHI SYSTEMS INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING;

ACTION PREDICTION; BATTLESPACE; COURSE OF ACTION; GRAY MATTER; INFORMATION SYNTHESIS; LABOUR-INTENSIVE; POSSIBLE FUTURES; PREDICTION MODELLING; STATE OF THE ART; SYNTHESIS PROCESS;

KNOWLEDGE ACQUISITION;

EID: 84966599470     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Behler, R. Maj Gen 2001, “Homeland Information: AOC Can Coordinate U.S. Terror Defense,” Defense News, 13.
2. Bell, B., Franke, J., and Mendenhall, H. 2000, “Leveraging Task Models for Team Intent Inference,” Proceedings of the International Conference on Artificial Intelligence.
3. Bell, B., Santos Jr., E., and Brown, S. M. 2002, “Making Adversary Decision Modeling Tractable with Intent Inference and Information Fusion”, Proceedings of the 11th Annual Computer Generated Force and Behavioral Representation Conference.
4. Fayette, D. F. 2001, “Effects-Based Operations: Application of new concepts, tactics, and software tools support the Air Force vision for effects-based operations”, Air Force Research Laboratory Technology Horizons, IF-00-15.
5. Franke, J., Brown, S. M., Bell, B., and Mendenhall, H. 2000, “Enhancing Teamwork Through Team-Level Intent Inference,” Proceedings of the International Conference on Artificial Intelligence.
6. Geddes, N. 1986, “The Use of Individual Differences in Inferring Human Operator Intentions,” Proceedings of the Second Annual Aerospace Applications of Artificial Intelligence Conference.
7. Hofmann, M., Chacon, D., Mayer, G., and Whitebread, K. 2001, “CAST Agents: Network-Centric Fires Unleashed.” 2001 National Fire Control Symposium: Session: Automation of the Kill Chain, Lihue, Hawaii.
8. Joint Publication 3-13 1998, Joint Doctrine for Information Operations, Department of Defense.
9. Joint Publication 2-01.3 2000, Joint Tactics, Techniques, and Procedures for Joint Intelligence Preparation of the Battlespace, Department of Defense.
10. Llinas, J., Drury, C., Bialas, W., and Chen, A.C. 1998, “Studies and Analyses of Vulnerabilities in Aided Adversarial Decision Making,” AFRL-HE-WP-TR-1998-0099.
11. Llinas, J., Drury, C., Jian, J. Y., Bisantz, A., and Younho Seong, Y. 1999, “Studies and Analyses of Aided Adversarial Decision Making Phase 2: Research on Human Trust in Automation,” AFRL-HE-WP-TR-1999-0216.
12. McCrabb, M., Concept of Operations for Effects-Based Operations 2000, Draft paper for AFRL/IFTB, Version 2.0.
13. McGrath, S., Chacon, D., and Whitebread, K. 2000, “Intelligent Mobile Agents in the Military Domain.” Proceedings of the Fourth International Conference on Autonomous Agents 2000.
14. Pearl, J. 1988, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann.
15. Saba, M.G., and Santos Jr., E. 2000, “The Multi-Agent Distributed Goal Satisfaction System,” Proceedings of the International ICSC Symposium on Multi-Agents and Mobile Agents in Virtual Organizations and E-Commerce (MAMA 2000), 389-394, Wollongong, Australia.
16. See, J. and Kuperman, G. 1997, “Information Warfare: Evaluation of Operator Information Processing Models,” AFRL-HE-WP-TR-1997-0166.
17. Whitebread, K. and Jameson, S. 1995, “Information Discovery in High-Volume, Frequently Changing Data,” IEEE Expert Journal – Intelligent Systems and Applications.