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Evaluation of Similarity Measures for Flight Simulator Training Scenarios

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Case-Based Reasoning Research and Development (ICCBR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12877))

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Abstract

Flight simulator training is fundamental for the acquisition and maintenance of professional pilot skills. One of the key factors for the effectiveness of this type of training is the design of the scripts of the sessions, usually called “scenarios”. Currently, civil aviation authorities are advocating a customization of the flight training scenarios based on the specific needs of each pilot, which makes their creation a very demanding task in time and resources. Automatic generation systems for these scenarios have been proposed in the scientific literature, but they have not been fully applied to commercial flight simulators yet.

In this paper, we review the most important advances in this field to date and introduce a first proposal of a case-based reasoning system for the generation of training scenarios for non-technical skills. Particularly, our goal is to evaluate a set of four different similarity measures for case retrieval of event sets found in these training scenarios, using the judgement of real experts in the field as validation method.

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References

  1. Allerton, D.: The impact of flight simulation in aerospace. Aeronaut. J. 114(1162), 747–756 (2010)

    Article  Google Scholar 

  2. Arthur, W., Jr., Bennett, W., Jr., Stanush, P.L., McNelly, T.L.: Factors that influence skill decay and retention: a quantitative review and analysis. Hum. Perform. 11(1), 57–101 (1998)

    Article  Google Scholar 

  3. Bowers, C., Jentsch, F., Baker, D., Prince, C., Salas, E.: Rapidly reconfigurable event-set based line operational evaluation scenarios. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 41, no. 2, pp. 912–915 (1997)

    Google Scholar 

  4. Childs, J.M., Spears, W.D.: Flight-skill decay and recurrent training. Percept. Mot. Skills 62(1), 235–242 (1986)

    Article  Google Scholar 

  5. Choi, S.S., Cha, S.H., Tappert, C.C.: A survey of binary similarity and distance measures. J. Syst. Cybern. Inform. 8(1), 43–48 (2010)

    Google Scholar 

  6. Curtis, M., Jentsch, F.: Line operations simulation development tools. In: Crew Resource Management, pp. 323–341. Elsevier (2019)

    Google Scholar 

  7. Flin, R., O’Connor, P.: Safety at the Sharp End: A Guide to Non-technical Skills. CRC Press, Boco Raton (2017)

    Book  Google Scholar 

  8. Fowlkes, J., Dwyer, D.J., Oser, R.L., Salas, E.: Event-based approach to training (EBAT). Int. J. Aviat. Psychol. 8(3), 209–221 (1998)

    Article  Google Scholar 

  9. IATA: Evidence-based training implementation guide July 2013. International Air Transport Association, Montreal (2013)

    Google Scholar 

  10. ICAO: Manual of evidence-based training. International Civil Aviation Organization, Montreal (2013)

    Google Scholar 

  11. ICAO: Doc 9868: Procedures for air navigation services. Training. Second Edition. International Civil Aviation Organization, Montreal (2015)

    Google Scholar 

  12. Jentsch, F., Bowers, C., Berry, D., Dougherty, W., Hitt, J.M.: Generating line-oriented flight simulation scenarios with the RRLOE computerized tool set. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 45, no. 8, pp. 749–749 (2001)

    Google Scholar 

  13. Kataria, A., Singh, M.: A review of data classification using k-nearest neighbour algorithm. Int. J. Emerg. Technol. Adv. Eng. 3(6), 354–360 (2013)

    Google Scholar 

  14. Koteskey, R.W., Hagan, C., Lish, E.T.: Line oriented flight training: a practical guide for developers. In: Crew Resource Management, pp. 283–322. Elsevier (2019)

    Google Scholar 

  15. Lesot, M.J., Rifqi, M., Benhadda, H.: Similarity measures for binary and numerical data: a survey. Int. J. Knowl. Eng. Soft Data Paradigms 1(1), 63–84 (2009)

    Article  Google Scholar 

  16. Loftin, R.B., Wang, L., Baffes, P., Hua, G.: An intelligent training system for space shuttle flight controllers. Telematics Inform. 5(3), 151–161 (1988)

    Article  Google Scholar 

  17. Loftin, R.B., Wang, L., Baffes, P., Rua, M.: An intelligent training system for payload-assist module deploys. In: Space Station Automation III, vol. 851, pp. 83–91. International Society for Optics and Photonics (1987)

    Google Scholar 

  18. Loftin, R., Wang, L., Baffes, P.: Intelligent scenario generation for simulation-based training. In: 7th Computers in Aerospace Conference, p. 3054 (1989)

    Google Scholar 

  19. Luo, L., Yin, H., Cai, W., Lees, M., Othman, N.B., Zhou, S.: Towards a data-driven approach to scenario generation for serious games. Comput. Anim. Vir. Worlds 25(3–4), 393–402 (2014)

    Article  Google Scholar 

  20. Luo, L., Yin, H., Cai, W., Lees, M., Zhou, S.: Interactive scenario generation for mission-based virtual training. Comput. Anim. Vir. Worlds 24(3–4), 345–354 (2013)

    Article  Google Scholar 

  21. Luo, L., Yin, H., Cai, W., Zhong, J., Lees, M.: Design and evaluation of a data-driven scenario generation framework for game-based training. IEEE Trans. Comput. Intell. AI Games 9(3), 213–226 (2016)

    Article  Google Scholar 

  22. Mansikka, H., Harris, D., Virtanen, K.: An input-process-output model of pilot core competencies. Aviation Psychology and Applied Human Factors (2017)

    Google Scholar 

  23. Martin, G.A.: Automatic scenario generation using procedural modeling techniques. Doctoral Dissertation. Ph.D. thesis, University of Central Florida (2012)

    Google Scholar 

  24. Page, R.L.: Brief history of flight simulation. In: SimTecT 2000 Proceedings, pp. 11–17 (2000)

    Google Scholar 

  25. Prince, C., Jentsch, F.: Aviation crew resource management training with low-fidelity devices. In: Improving Teamwork in Organizations: Applications of Resource Management Training, pp. 147–164 (2001)

    Google Scholar 

  26. Rowe, J., Smith, A., Spain, R., Lester, J.: Understanding novelty in reinforcement learning-based automated scenario generation. In: Proceedings of the 7th Annual GIFT Users Symposium, p. 76. US Army Combat Capabilities Development Command-Soldier Center (2019)

    Google Scholar 

  27. Salas, E., Priest, H.A., Wilson, K.A., Burke, C.S.: Scenario-based training: improving military mission performance and adaptability. Mi. Life: Psychol. Serv. Peace Combat 2, 32–53 (2006)

    Google Scholar 

  28. Salas, E., Prince, C., Baker, D.P., Shrestha, L.: Situation awareness in team performance: implications for measurement and training. Hum. Factors 37(1), 123–136 (1995)

    Article  Google Scholar 

  29. Salas, E., et al.: Simulation-based training for patient safety: 10 principles that matter. J. Patient Saf. 4(1), 3–8 (2008)

    Article  Google Scholar 

  30. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295 (2001)

    Google Scholar 

  31. Sottilare, R.: A hybrid machine learning approach to automated scenario generation (ASG) to support adaptive instruction in virtual simulations and games. In: The International Defense & Homeland Security Simulation Workshop of the I3M Conference, Budapest, Hungary (2018)

    Google Scholar 

  32. Zook, A., Lee-Urban, S., Riedl, M.O., Holden, H.K., Sottilare, R.A., Brawner, K.W.: Automated scenario generation: toward tailored and optimized military training in virtual environments. In: Proceedings of the International Conference on the Foundations of Digital Games, pp. 164–171. ACM (2012)

    Google Scholar 

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Acknowledgements

This work has been possible thanks to the collaboration of the EASA approved training organization Global Training and Aviation, S.L.

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Authors and Affiliations

  1. Departamento de Ingeniería Industrial y Aeroespacial, Universidad Europea de Madrid, c/ Tajo s/n. Urb. El Bosque, 28670, Villaviciosa de Odón, Madrid, Spain

    Rubén Dapica

  2. Departamento de Ingeniería del Software e Inteligencia Artificial, Universidad Complutense de Madrid, c/ Profesor José García Santesmases 9, 28040, Madrid, Madrid, Spain

    Federico Peinado

Authors
  1. Rubén Dapica
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  2. Federico Peinado
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Correspondence to Rubén Dapica .

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Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Antonio A. Sánchez-Ruiz

  2. Knexus Research Corp., National Harbor, MD, USA

    Michael W. Floyd

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Dapica, R., Peinado, F. (2021). Evaluation of Similarity Measures for Flight Simulator Training Scenarios. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-86957-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86956-4

  • Online ISBN: 978-3-030-86957-1

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