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Representing Uncertainty in Computer-Generated Forces

Authors: Ruibiao J. Guo, Brad Cain, Pierre Meunier

Abstract:

The Integrated Performance Modelling Environment (IPME) is a powerful simulation engine for task simulation and performance analysis. However, it has no high level cognition such as memory and reasoning for complex simulation. This article introduces a knowledge representation and reasoning scheme that can accommodate uncertainty in simulations of military personnel with IPME. This approach demonstrates how advanced reasoning models that support similarity-based associative process, rule-based abstract process, multiple reasoning methods and real-time interaction can be integrated with conventional task network modelling to provide greater functionality and flexibility when modelling operator performance.

Keywords: Computer-Generated Forces, Human Behaviour Representation, IPME, Modelling and Simulation, Uncertainty Reasoning

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327895

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References:


[1] R.W. Pew and A.S. Mavor, A.S., Modelling Human and Organizational Behaviour : Application to Military Simulations. The National Academy Press. Washington, D.C., 1998.
[2] M.Tambe, W.L. Johnson, R.M. Jones, F. Koss, J.E. Laird, P.S. Rosenbloom, and K. Schwamb, "Intelligent agents for interactive simulation environments," AI Magazine, 1995(Spring), pp.15-37.
[3] R.W. Hill, J. Chen, J. Grace, P. Rosenbloom, and M. Tambe, "Intelligent agents for the synthetic battlefield: a company of rotary wing aircraft," Proceedings of the Ninth Conference on the Innovative Applications of Artificial Intelligence, 1006-1012. Menlo Park, Calif.: AAAI Press, 1997, pp.1006-1012.
[4] J. Gratch, and R.W. Jr Hill, "Continuous planning and collaboration for command and control in joint synthetic battlespaces," Proceedings of the 8th Conference on Computer Generated Forces and Behaviour al Representation, Orlando, FL., 1999.
[5] R.M. Jones, J.E. Laird, P.E. Nielsen, J.J. Coulter, P. Kenny, and F.V. Koss, "Automated intelligent pilots for combat flight simulation," AI Magazine, Vol. 20, No.1, 1999, pp. 27-41.
[6] P.A.M Ehlert, Q.M. Mouthaan, and L.J.M. Rothkrantz, "A rule-based and a probabilistic system for situation recognition in a flight simulator," In Mehdi, Q., Gough, N. and Natkin, S. (Eds.) Proceedings of the 4th Int. Conf. on Intelligent Games and Simulation (GAME-ON 2003), London, Great Britain: Eurosis, 2003, pp. 201-207.
[7] S. Mulgund, K. Harper, G. Zacharias, and T. Menke, "SAMPLE: situation awareness model for pilot-in-the-loop evaluation," Proceedings of the 9th Conference on Computer Generated Forces and Behaviour Representation, Orlando, FL, 2000.
[8] J.A. Doyal and B.E. Brett, "Enhancing training realism by incorporating high-fidelity human behaviour representations in the synthetic battlespace," Proceedings of the Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, FL., 2003.
[9] C. Burdick, H. Graf, J. Huynh, M. Grell, D. MacQueen, H. Argo, P. Bross, J. Prince, H. Johnson, and H.R. Blacksten, "Heterogeneous agent operations in JWARS," Proceedings of the 2003 Conference on Behaviour Representation in Modelling and Simulation (BRIMS 2003), Scottsdale, Arizona, 2003.
[10] W.K. Stevens and L. Parish, "Representation of unit level behaviour in the naval simulation system (NSS)," Proceedings of the Defense Modelling and Simulation Workshop on Unit Level Behaviour, Alexandria, VA: Defense Modelling and Simulation Office, August 7-8, 1996.
[11] B. Cain, and P. Kwantes, "Architectural characteristics for flexible CGF: Integrating HBR," Internal Technical Communication, 2004.
[12] R.J. Guo, B. Cain, and P. Meunier, "Knowledge representation supporting multiple reasoning methods for simulated operators," Proceedings of the 2005 Conference on Behaviour Representation in Modelling and Simulation (BRIMS 2005), Universal City, CA, May 16- 19, 2005.
[13] D. Vakas, J. Prince, and H.R. Blacksten, "Commander behaviour and course of action selection in JWARS," Proceedings of the 2001 Winter Simulation Conference. Peters, B.A., Smith, J.S., Medeiros, D.J. & Rohrer, M.N. eds, 2001, pp. 697-705.
[14] C.G. Looney, and L.R. Liang, L.R, "Cognitive situation and threat assessments of ground battlespaces," Information Fusion. Vol. 4 (Issue 4), 2003, pp. 297-308.
[15] J. Brynielsson, "Game-theoretic reasoning and command and control," Proceedings of the 15th Mini-EURO Conferences: Managing Uncertainty in Decision Support Models. Coimbra, Portugal, Sept. 22- 24, 2004.
[16] L. Yu, "A Bayesian network representation of a submarine commander," Proceedings of the 2003 Conference on Behaviour Representation in Modelling and Simulation (BRIMS). Scottsdale, Arizona, 2003.
[17] J. Moffat, and S. Witty,"Bayesian decision making and military command and control," Journal of the Operational Research Society. Vol. 53, 2002, pp.709-718.
[18] O.J. Mengshoel, and D.C. Wilkins, "Filtering and visualizing uncertain battlefield data using Bayesian networks," Proceedings of the ARL Advanced Displays and Interactive Displays Federated Laboratory Second Annual Symposium. College Park, MD, 1998.
[19] G.J. Jeram, "Open design for helicopter active control systems," Presented at the American Helicopter Society (AHS) 58th Forum. Montreal, June 10-13, 2002.
[20] S. Mulgund, G. Rinkus, C. Illgen, and J. Friskie, "OLIPSA: on-line intelligent processor for situation assessment," Second Annual Symposium and Exhibition on Situational Awareness in the Tactical Air Environment, Patuxent River, MD,1997.
[21] L.E.P. Blasch, "Learning attributes for situational awareness in the landing of an autonomous airplane," Proceedings of the Digital Avionics Conference, San Diego, CA, October, 1997, pp. 5.3.1-5.3.8.
[22] Y. Zeyada, and R.A. Hess, "Modelling human pilot cue utilization with applications to simulator fidelity assessment," Journal of Aircraft. 2000 Jul- 37(4), Aug. 2000, pp. 588-97.
[23] J.F. Montgomery, and A. Bekey, "Learning helicopter control through "teaching by showing", Proceedings of the IEEE Conference on Decision and Control, 4, 1998, pp3467 - 3652.
[24] R. Hamaza and S. Menon, "Advanced knowledge management for helicopter HUMS," Proceedings of the DSTO International Conference on Health and Usage Monitoring, Melbourne, Australia. February 2001.
[25] E. Tunstel, A. Howard, and H. Seraji, "Rule-based reasoning and neural network perception for safe off-road robot mobility," Expert Systems: The International Journal of Knowledge Engineering and Neural Networks, Vol. 19, No. 4, September 2002, pp. 191-200.
[26] B. Kadmiry, Fuzzy Control for an Unmanned Helicopter. Thesis of Linkopings Universitet. Linkoping, Sweden, 2002.
[27] G. Buskey, J. Roberts, and G. Wyeth, "Online learning of autonomous helicopter control," Proceedings of 2002 Australasian Conference on Robotics and Automation. Auckland, November 27-29, 2002.
[28] G.Z. Panagiotis, and S.G. Tzafestas, "Motion control for mobile robot obstacle avoidance and navigation: a fuzzy logic-based approach," System Analysis Modelling Simulation. Vol. 43, Issue 12, 2003, pp. 1625-1637.
[29] G. Fayad and P. Webb, "Optimized fuzzy logic based algorithm for a mobile robot collision avoidance in an unknown environment," Proceedings of the 7th European Congress on Intelligent Techniques & Soft Computing, Aachen, Germany, September 13-16, 1999.
[30] R. A. Richards, "Application of multiple artificial intelligence techniques for an aircraft carrier landing decision support tool, IEEE 2002 World Congress on Computational Intelligence, Honolulu, Hawaii, USA. May 12-17, 2002.
[31] L. Abi-Zeif, and J.R. Frost, "SARPlan: A decision support system for Canadian Search and Rescue Operations," European Journal of Operational Research, 162(3), 2005, pp.630-653.
[32] M. Asunci├│n, L. Castillo, J. Fernandez-Olivares, O. Garcia-Perez, A. Gonzalez, and F. Palao, "Handling fuzzy temporal constraints in a planning environment," Annals on Operations Research, special issue on "Personnel Scheduling and Planning", 2004.
[33] A. Howard, E.Tunstel, D. Edwards, and A. Carlson, "Enhancing fuzzy robot navigation systems by mimicking human visual perception of natural terrain traversability," Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPA International Conference, Vancouver, B.C., Canada, July 2001, pp. 7-12.
[34] R.W. Picard, "Affective computing," M.I.T. Media Laboratory Perceptual Computing Section Technical Report No. 321, 1995.
[35] M.S. El-Nasr, J. Yen, and T.R. Ioerger, "FLAME - fuzzy logic adaptive model of emotions," Autonomous Agents and Multi-Agent Systems. Vol. 3, Issue 3, 2000, pp 219-257.
[36] R.J. Guo, B. Cain, and P. Meunier, Supporting uncertainty reasoning in simulated operators for networks. DRDC TR 2005-268, 2005.
[37] G.S. Halford, M. Ford, J. Busby, & G. Andrews, "Literature Review of Formal Models of Human Thinking. DRDC CR 2006-206, 2006.
[38] J.B.T. Evans, In two minds: dual-process accounts of reasoning. TRENDS in Cognitive Science, Vol. 6 (No. 10), 2003, pp 454-459,.
[39] S.A. Sloman, The Empirical Case for Two Systems of Reasoning. Psychological Bulletin, Vol. 119 (No. 1), 1996, pp. 3-22.
[40] S.A. Sloman, Two systems of reasoning. In T. Gilovich & D. Griffin (Eds.), Heuristics and biases: The psychology of intuitive judgment, New York, NY: Cambridge University Press, 2002, pp. 379-396.