A Soft Computing Approach to Model Human Factors in Air Warfare Simulation System

  • D. Vijay Rao
  • Dana Balas-Timar
Part of the Studies in Computational Intelligence book series (SCI, volume 561)


With increasing defence budget constraints and environmental safety concerns on employing live exercises for training, there has been a reinforced focus end considerable efforts on designing military training simulators using modelling, simulation, and analysis for operational analyses and training. Air Warfare Simulation System is an agent-oriented virtual warfare simulator that is designed using these concepts for operational analysis and course of action analysis for training. A critical factor that decides the next course of action and hence the results of the simulation is the skill, experience, situation awareness of the pilot in the aircraft cockpit and the pilots’ decision making ability in the cockpit. Advances in combat aircraft avionics and onboard automation, information from onboard and ground sensors and satellites poses a threat in terms of information and cognitive overload to the pilot, and triggering conditions that makes decision making a difficult task. Several mathematical models of the pilot behaviour, typically based on control theory, have been proposed in literature. In this work, we describe a novel approach based on soft computing and Computational Intelligence paradigms called ANFIS, a neuro-fuzzy hybridization technique, to model the pilot agent and its behaviour characteristics in the warfare simulator. This emerges as an interesting problem as the decisions made are dynamic and depend upon the actions taken by enemy. We also build a pilots’ database that represents the specific cognitive characteristics, skills, training experience, and as factors that affect the pilot’s decision making and study its effect on the results obtained from the warfare simulation. We illustrate the methodology with suitable examples and lessons drawn from the virtual air warfare simulator.


Agent-based simulation Situation-awareness Decision making Cognitive overload Neuro-fuzzy hybridization Military warfare analysis 


  1. 1.
    Vijay Rao, D.: The Design of Air Warfare Simulation System. Technical report, Institute for Systems Studies and Analyses (2011)Google Scholar
  2. 2.
    Vijay Rao, D., Kaur, J.: A Fuzzy Rule-based approach to design game rules in a mission planning and evaluation system, In: 6th IFIP Conference on Artificial Intelligence Applications and Innovations, Springer, New York (2010)Google Scholar
  3. 3.
    Vijay Rao, D., Saha, B.: An Agent oriented Approach to Developing Intelligent Training Simulators. In: SISO Euro-SIW Conference, Ontario, Canada (June 2010)Google Scholar
  4. 4.
    Vijay Rao, D., Iliadis, L. Spartalis, S. Papaleonidas, A.: Modelling Environmental factors and effects in virtual warfare simulators by using a multi agent approach. Int. J. Artif. Intell. 9(A12), pp. 172–185 (2012)Google Scholar
  5. 5.
    Vijay Rao, D. Iliadis, L. Spartalis, S.: A Neuro-Fuzzy Hybridization Approach to Model Weather Operations in a Virtual Warfare Analysis System”. In: Proceedings of the 12th EANN (Engineering Applications of Neural Networks). LNCS AICT, vol. 363(1), pp. 111–121. Springer (2011)Google Scholar
  6. 6.
    Vijay Rao, D. Ravi, S. Iliadis, L. Sarma, V.V.S.: “An Ontology based approach to designing adaptive lesson plans in military training simulators”. In: Proceedings of the 13th EANN (Engineering Applications of Neural Networks), LNCS AICT, vol. 363(1), pp. 81–93. Springer (2012)Google Scholar
  7. 7.
    McRuer, D.T. Krendel, E.S.: Mathematical models of human pilot behavior, AGARD monograph No. 188, NATO advisory group on Aerospace research and development. (AGARD), London (1974)Google Scholar
  8. 8.
    Banks, J.: Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. Wiley, New York (1998)CrossRefGoogle Scholar
  9. 9.
    Cox, E.: The Fuzzy Systems Handbook, 2nd edn. Academic Press, New York (1999)Google Scholar
  10. 10.
    Taher, J. Zomaya, A.Y.: In: Zomaya, A.Y. (Ed.)Artificial Neural Networks in Handbook of Nature-Inspired and Innovative Computing, Integrating Classical Models with Emerging Technologies, pp. 147–186, Springer USA (2006)Google Scholar
  11. 11.
    Jang, J.S.R.: ANFIS: Adaptive network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (May/June 1993)Google Scholar
  12. 12.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational approach to Learning and Machine Intelligence. Prentice Hall, Mahwah (1997)Google Scholar
  13. 13.
    Mitra, S., Yoichi, Hayashi: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans. Neural Netw. 11(3), 748–768 (2000)CrossRefGoogle Scholar
  14. 14.
    Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems-Introduction and New Directions. Prentice Hall PTR, Upper Saddle River, USA (2001)MATHGoogle Scholar
  15. 15.
    Endsley, M.R., Garland, D.J. (eds.): Situation Awareness Analysis and Measurement. Lawrence Erlbaum Associates Publishers, Mahwah (2000)Google Scholar
  16. 16.
    Karray, F.O., De Silva, C.: Soft Computing and Intelligent Systems-Theory. Tools and Applications, Pearson-Addison Wesley, England (2004)Google Scholar
  17. 17.
    MATLAB Fuzzy Logic Toolbox Mathworks
  18. 18.
    Nunn,W.R. Oberle, R.A.: Evaluating Air Combat Maneuvering Engagements, Methodology, Center for Naval Analyses, 1402 vol. I Wilson Boulevard, Arlington, Virginia 22209, USA (1976)Google Scholar
  19. 19.
    Parrott, E.: Combat Performance Advantage: Method of Evaluating Air Combat Performance Effectiveness, Aerodynamics and Performance Branch, Technical report ASD-TR-78-364,Aeronautical Systems Division, Air Force Systems Command, Wright-Patterson AFB, Ohio 45433, USA (1978)Google Scholar
  20. 20.
    Triantaphyllou, E. Mann, S.H.: An Examination of the Effectiveness of Multi-Dimensional Decision-Making Methods: A Decision-Making Paradox, Decision Support Systems vol. 5, 303–312, North Holland (1989)Google Scholar
  21. 21.
    Jaiswal, N.K.: Military Operations Research: Quantitative Decision Making. Kluwer Academic Publishers, Boston (1997)Google Scholar
  22. 22.
    Markushostmanna, Bernauer, Hans-Joachimmosler, T.: Peterreichert, Bernhardtruffer, multi-attribute value theory as a framework for conflict resolution in river Rehabilitation. J. Multi-Crit Decis. Anal. 13, 91–102 (2005)CrossRefGoogle Scholar
  23. 23.
    Tolk, A.: Engineering Principles of Combat Modeling and Distributed Simulation. Wiley, USA (2012)CrossRefGoogle Scholar
  24. 24.
    Rodin, E.Y., Amin, S.M.: Maneuver prediction in air combat via artificial neural networks. Comput. Math. Appl. 24, 95–112 (1992)CrossRefGoogle Scholar
  25. 25.
    Gorman, P.R., Sejnowski, T.J.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netw. 1, 75–89 (1988)CrossRefGoogle Scholar
  26. 26.
    N.H. Farhat, S. Miyahara, K.S. Lee, Optical Analog of Two-Dimensional Neural Networks and their Applications in Recognition of Radar Targets, pp. 146–152. American Institute of Physics, New York (1986)Google Scholar
  27. 27.
    Meier, C.D. Stenerson, R.O.: Recognition networks for tactical air combat maneuvers, In: Proceedings of the 4th Annual AAAI Conference, Dayton, OH (Oct. 1988)Google Scholar
  28. 28.
    Mitchell, R.R.: Expert systems and air-combat simulation. AI Expert 4(9), pp. 38–43 (Sept. 1989)Google Scholar
  29. 29.
    Morgan, A.J.: Predicting the behaviour of dynamic systems with qualitative vectors. In: Hallam, J. Mellish C. (eds.) Advances in Artificial Intelligence, pp. 81–95. Wiley, New York (1988)Google Scholar
  30. 30.
    Tran, C. Abraham, A. Jain, L.: Adaptation of a Mamdani Fuzzy Inference System Using Neuro-genetic Approach for Tactical Air Combat Decision Support System, AI 2002: Advances in Artificial Intelligence (2557). In: Proceedings 15th Australian Joint Conference on Artificial Intelligence Canberra, pp. 672–680 Springer, Australia 2–6 Dec 2002Google Scholar
  31. 31.
    Heinze, C.: Modelling Intention Recognition for Intelligent Agent Systems, DSTO-RR-0286, DSTO Systems Sciences Laboratory, Edinburgh, South Australia, Australia 5111, (ADA430005), (2004)Google Scholar
  32. 32.
    Wooldridge, M., Jennings, N.R.: Intelligent agents:theory and practice. Knowl. Eng. Rev. 10(12), 115–152 (1995)CrossRefGoogle Scholar
  33. 33.
    Bratman, Michael E.: Intention, Plans, and Practical Reasoning. Harvard University Press, Cambridge (1987)Google Scholar
  34. 34.
    Rao A.S. Georgeff, M.P. :An Abstract Architecture for Rational Agents. In: Rich, C. Swartout, W. Nebel, B. (eds.) Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning (KR’92), Morgan Kaufmann Publishers, San Francisco, CA, USA (1992)Google Scholar
  35. 35.
    Endsley, M.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 31(l), 32–64 (1995)Google Scholar
  36. 36.
    Endsley, M.: The role of situation awareness in naturalistic decision making. In: Zsambok C.E. Klein, G. (eds.) Naturalistic Decision Making. Lawrence Erlbaum Associates Publishers, USA (1997)Google Scholar
  37. 37.
    McManus, J.W. Goodrich, K.H. Artificial Intelligence Based Tactical Guidance For Fighter Aircraft. In: AIAA Guidance, Navigation, and Control Conference 20–22 August 1990, Portland, Oregon, USA (1990)Google Scholar
  38. 38.
    Heinze, C. Smith, B. Cross M.: Thinking quickly: agents for modeling air warfare. In Proceedings of the 9th Australian Joint Conference on Artificial Intelligence (AP98), Brisbane, Australia (1998)Google Scholar
  39. 39.
    Rao, A.S. Murray, G.: Multi-agent mental-state recognition and its application to air-combat modeling. In: Proceedings of the 13th International Workshopon Distributed Artificial Intelligence (DAI-94), pp. 283–304. Seattle, WA, USA (1994)Google Scholar
  40. 40.
    Rao, A.: A unified view of plans as recipes. Technical Report 77, Australian Artificial Intelligence Institute, Melbourne, Australia, August (1997)Google Scholar
  41. 41.
    Rao, A.S.: Means-end plan recognition—towards a theory of reactive recognition. In Doyle, J. Sandewall, E. Torasso, P. (eds.) Proceedings of 4th International Conference on Principles of Knowledge Representation and Reasoning, pp. 497–508, Bonn, FRG, May Morgan Kaufmann Publishers; San Francisco, CA, USA (1994)Google Scholar
  42. 42.
    Rao, A.S. Georgeff, M.P.: Modeling rational agents within a bdi-architecture. In: Second International Conference on Principles of Knowledge Representation and Reasoning, San Mateo, CA, (1991)Google Scholar
  43. 43.
    Rao, A.S. Georgeff, M.P.: BDI-agents: from theory to practice, In: Proceedings of the First International Conference on Multiagent Systems, San Francisco (1995)Google Scholar
  44. 44.
    Rao, A.S. Georgeff, M.P.: Formal models and decision procedures for multi-agent systems, Technical report Technical Note 61, Australian AI Institute, 171 La Trobe Street, Melbourne, Australia (1995)Google Scholar
  45. 45.
    Weerasooriya, D. Rao, A. Ramamohanarao, K.: Design of a concurrent agent oriented language. In: Wooldridge M. Jennings N.R. (eds.) Intelligent Agents: Theories, Architectures, and Languages (LNAI), vol. 890, pp. 386–402. Springer, Heidelberg, Germany (1995)Google Scholar
  46. 46.
    Goodrich, K.H. McManus, J.W.: Development of A Tactical Guidance Research and Evaluation System(TGRES). AIAA Paper #89–3312, (August 1989)Google Scholar
  47. 47.
    McManus, J.W. Goodrich, K.H.: “Application of Artificial Intelligence (AI) Programming Techniques to Tactical Guidance for Fighter Aircraft.” AIAA Paper #89–3525, (August 1989)Google Scholar
  48. 48.
    Goodrich, K.H. McManus J.W.: “An Integrated Environment For Tactical Guidance Research and Evaluation.” AIAA Paper #90–1287 (May 1990)Google Scholar
  49. 49.
    McManus, J.W. : “A Parallel Distributed System for Aircraft Tactical Decision Generation.” In: Proceedings of the Digital Avionics Systems Conference, Virginia Beach, VA (October 1990)Google Scholar
  50. 50.
    Ni, H.: Penny : BlackBoard Systems: The Blackboard Model of Problem solving and the Evolution Of Blackboard Architectures. AI Mag. 7(3), 38–53 (1986)Google Scholar
  51. 51.
    Burgin, G.H. et al.: An Adaptive Maneuvering Logic Computer Program for the Simulation of One-on-One Air-to-Air Combat. (NASA) vol I–II, pp. CR-2582–CR-2583 (1975)Google Scholar
  52. 52.
    Michael, W.: Intelligent agents. In: Weiss, G. (ed) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 27–78. The MIT Press, Cambridge, USA (1999)Google Scholar
  53. 53.
    Lucas, A., Goss, S., The Potential For Intelligent Software Agents in defence simulation. In: Proceedings of the Information, Decision and Control, pp. 579–583, Adelaide, SA (1999)Google Scholar
  54. 54.
    Norling, E.J. Modelling Human Behaviour with BDI Agents, PhD. Thesis, Department of Computer Science and Engineering, University of Melbourne, (June 2009)Google Scholar
  55. 55.
    Boril, J. Jalovecky, R.: Experimental Identification of Pilot Response Using Measured Data from a Flight Simulator In: Proceedings of the 13th EANN (Engineering Applications of Neural Networks), LNCS AICT vol. 363(1), Springer, Berlin (2012)Google Scholar
  56. 56.
    Boril, J. Jalovecky, R.: Response of the Mechatronic System, Pilot—Aircraft on Incurred Step Disturbance. In: 53rd International Symposium ELMAR-2011, pp. 261–264. ITG, Zagreb (2011)Google Scholar
  57. 57.
    Jalovecky, R. Janu, P.: Human—Pilot’s Features During Aircraft Flight Control from Automatic Regulation Viewpoint. In: 4th International Symposium on Measurement, Analysis and Modeling of Human Functions, pp. 119–123. Czech Republic: Czech Technical University in Prague, Prague (2010)Google Scholar
  58. 58.
    Jalovecky, R.: Man in the Aircraft’s Flight Control System. Adv. Mil. Technol.—J. Sci., vol. 4(1), pp. 49–57 (2009)Google Scholar
  59. 59.
    Cameron, N., Thomson, D.G., Murray-Smith, D.J.: Pilot Modelling and Inverse Simulation for Initial Handling Qualities Assessment. Aeronaut.J. 107(1744), 511–520 (2003)Google Scholar
  60. 60.
    Boril, J. Jalovecky, R.: Simulation of Mechatronic System Pilot—Aircraft—Oscillation Damper. In: ICMT´11—International Conference on Military Technologies, pp. 591–597. University of Defence, Brno (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Institute for Systems Studies and AnalysesDefence Research and Development OrganisationDelhiIndia
  2. 2.Faculty of Educational Sciences, Psychology and Social SciencesAurel Vlaicu University of AradAradRomania

Personalised recommendations