Abstract
A future challenge for the European air traffic system is an estimated rise in traffic load in combination with the occurrence of so-called disruptive events (e.g., thunderstorms, security alarms or union strikes). EUROCONTROL showed in their simulations that external events lead to instabilities in the whole European air traffic system with a weakened ability to recover [cf. (EUROCONTROL, Challenges of growth 2013—Task 6: the effect of air traffic network congestion 2035. EUROCONTROL, Brussels, 2013)]. Due to these events, EUROCONTROL estimates a strong increase of delay within the air traffic system, especially at the airports. Among various factors to keep the impact of these events minimized, a sufficient cooperation between the affected air traffic participants (pilots, controllers, airport staff, etc.) is necessary. Sharing information and aligning on a procedure to utilize the existing resources offers a way to efficiently keep up operations. A tool to train this cooperative behavior and to validate the benefits of new systems and procedures regarding the enforcement of cooperative behavior is a human-in-the-loop simulation (HITL). HITL simulations enable cooperative situations by facing the participants with disruptive events. These situations are commonly supported by so-called pseudo-actors, humans which take on one or multiple roles within the cooperation. Thereby the cooperation can be developed in a more or less challenging way. For instance, a delayed pilot reaction in dense traffic or an airline dispatcher not willing to cancel flights into a congested airport can be performed by a pseudo-actor. Currently, pseudo-actors with predefined instructions for their behavior are the only way to generate the above-mentioned challenges in HITL simulations. As human beings, the pseudo-actors show varying behavior. Therefore, reproducibility of the HITL simulation is limited, although being one of the key features. In contrast to field trials in a real environment all influencing factors of a HITL simulation are under control. Situations can be repeated multiple times. In terms of training simulation this is necessary to generate the intended learning effect. In terms of validation simulation the possibility to exactly repeat a situation is needed to compare a situation with the support of a new system (experimental condition) to one without the new system (baseline). This paper provides a contribution for assessing the potential of human pseudo-actor alternatives. Virtual agents are already used in other fields such as gaming and other types of simulation (e.g., virtual military training simulation). These virtual agents interact with the other human participants of the simulation. Moreover, as being computer applications, they strictly do follow their programmed behavior. Thus, they offer the benefit of reproducible behavior. The evaluation within this paper uses the airport management simulation of the German Aerospace Center to challenge four participants with multiple disruptive events. The role of the stand and gate allocation manager is either taken by a human pseudo-actor or a virtual agent. The results of the simulation runs with the human pseudo-actor are compared to the ones with the virtual agent. Analyzing the results a first conclusion on the potential of virtual agents in air traffic HITL simulations will be given.
Similar content being viewed by others
References
EUROCONTROL: Challenges of growth 2013—Task 6: the effect of air traffic network congestion 2035. EUROCONTROL, Brussels (2013)
Makins, N.: EUROPEAN Operational Concept Validation Methodology (EOCVM). EUROCONTROL, version 3.0. Brussels, Belgium (2005) [Online]. Available at: https://www.eurocontrol.int/sites/default/files/publication/files/e-ocvm3-vol-1-022010.pdf. Accessed 4 July 2017
Dow, C., Histon, J.: An Air Traffic Control Simulation Fidelity Definition and Categorization System. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 92–96. SAGE Publications, Los Angeles, CA (2014)
Valverde, H.H.: A review of flight simulator transfer of training studies. Human Fact. 15(6), 510–522 (1973)
Helmreich, R.L., Ashleigh, C.M., John, A.W.: The evolution of crew resource management training in commercial aviation. Int. J. Aviat. Psychol. 9(1), 19–32 (1999)
Suikat, R., Kaltenhäuser, S., Weber, B., Hampe, J., Timmermann, F.: ACCES—A Gaming and Simulation Platform for Advanced Airport Operations Concepts. AIAA Modeling and Simulation Technologies, Toronto (2010)
Narayanan, S., Kidambi, P.: Interactive simulations: history, fetures, and trends. In: Rothrock, L., Narayanan, S. (eds.) Human-in-the-loop simulations: methods and practice, Springer Press, New York (2011) (ISBN: 978-0857298829)
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1), 32–64 (1995)
VDI/VDE 2653: Multi agent systems in industrial automation—fundamentals, part 1, Verein Deutscher Ingenieure, ICS 35.240.50, June (2010)
Vakarelov, O.: The cognitive agent: overcoming informational limits. Adapt Behav 19(2), 83–100 (2009)
Huet, D., Booth, D., Pickup, S.: A-CDM impact assessment. EUROCONTOL, Brussels, Belgium [Online] (2016). https://www.eurocontrol.int/sites/default/files/publication/files/a-cdm-impactassessment-2016.pdf. Accessed 4 July 2017
EUROCONTROL: Airport CDM Implementation—The Manual, version 4.0. Brussels, Belgium [Online]. (2012). https://www.eurocontrol.int/sites/default/files/publication/files/2012-airport-cdm-manual-v4.pdf. Accessed 4 July 2017
Papenfuss, A., Carstengerdes, C., Günther, Y.: “Konzept zur Kooperation in Flughafen-Leitständen.” In: 57. FAS DGLR L6.4 Anthropotechnik conference proceedings, 2015, Rostock
Schier, S., Timmermann, F.: Pett, T.: Airport management in the box—a human in the loop simulation for ACDM and airport management, Deutscher Luft- und Raumfahrt Kongress. Braunschweig, Deutschland 2016, 13.-15. Sept. 2016, Braunschweig, Deutschland (2016)
Schier, S., Pett, T., Mohr, O., Yeo, S.J.: Design and evaluation of user interfaces for an airport management simulation. AIAA Aviation Modelling and Simulation Conference, Washington D.C. (2016)
Schier, S., Papenfuß, A., Carstengerdes, N.: “Modellierung komplexer Flughafen-Ereignisse für Human-In-The-Loop Simulationen”, Simulation technischer Systeme inklusive der Grundlagen und Methoden in Modellbildung und simulation. Ulm, Deutschland (2017) (09–10, Mar 2017)
Schier, S., Rosenau, L., Freese, M.: Trainieren mit CLAUDI—Das Potential kognitiver Agenten für Simulationen und Planspiele. 6. Interdisziplinärer Workshop Kognitive Systeme, München, Deutschland (27–29, Mar 2017)
Charness, G., Gneezy, U., Kuhn, M.A.: Experimental methods: between-subject and within-subject design.. J. Econ. Behav. Organ. 81(1), 1–8 (2012)
Brooke, J.: SUS-A quick and dirty usability scale. In: Jordan, P., Jordan, W. (eds.) Usability Evaluation in Industry, pp. 4–7. Taylor & Francis, London (1996)
Rothaug, J.: Age, experience and automation in european air traffic control-survey in the ECAC Area. EUROCONTROL (2004) [Online]. Available: https://www.eurocontrol.int/sites/default/files/field_tabs/content/documents/nm/safety/safety-survey-in-theecac-area.pdf. Accessed 4 July 2017
Tielmann, M., Brinkmann, W., Neerincx, M.: 4TU. Center for Research Data. http://data4tu.nl/repository/uuid:30db1be5-5070-4c7f-87af-210e09bda1c4 (at 04.07.2017; 19.05.2017)
Papenfuss, A., Carstengerdes, N., Schier, S., Günther, Y.: What to say when: Guidelines for Decision Making. An evaluation of a concept for cooperation in an APOC”, Twelfth USA/Europe Air Traffic Management Research and Development Seminar (ATM2017), 26–30, June 2017, Seatlle, USA (2017)
Brooke, J.: SUS: a retrospective. J. Usability Stud. 8(2), 29–40 (2013)
Piekert, F., Schier, S., Marsden, A., Carstengerdes, N., Suikat, R.: A high-fidelity artificial airport environment for SESAR APOC validation experiments. J. Air Transp. Stud. 8(1), 31–50 (2015)
Turing, A.M.: Computing machinery and intelligence. Mind 49, 433–460 (1950)
Saenz, A.: Cleverbot chat engine is learning from the internet to talk like a human. Singularity Hub (2010) [Online]. Available at: https://singularityhub.com/2010/01/13/cleverbot-chat-engine-is-learning-fromthe-internet-to-talk-like-a-human/. Accessed 4 July 2017
Warwick, K., Shah, H.: Can machines think? A report on turing test experiments at the royal society. J. Exp. Theor. Artif. Intell. (2015) https://doi.org/10.1080/0952813X.2015.1055826
Doris, M.. Dehn: Assessing the impact of automation on the air traffic controller: the SHAPE Questionnaires. Air Traffic Control Q. 16(2), 127–146 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Schier, S., Duensing, A. & Britze, S. Sparring partners for human-in-the-loop simulations: the potential of virtual agents in air traffic simulations. CEAS Aeronaut J 10, 553–564 (2019). https://doi.org/10.1007/s13272-018-0335-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13272-018-0335-y