Abstract
To accurately replicate the procedures and actions of piloting an aircraft, it is important to create an intelligent system capable of analyzing and executing tasks using established protocols in the field. In this study, we introduce a cognitive agent based on the ACT-R cognitive architecture that incorporates an ontological reference model into its declarative memory. The purpose of this is to simulate the performance of critical piloting tasks, such as take-off, in a manner similar to that of a human pilot. The agent accomplishes this by utilizing production rules stored in its procedural memory to deduce knowledge captured and formalized by the ontological reference model stored in its declarative memory. Our findings suggest that this approach is a key step towards developing a cognitive agent that can be tested in a real flight simulator, providing insights into how human pilots function in terms of their cognitive and affective behavior.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Varela, F.J., Thompson, E., Rosch, E.: The Embodied Mind: Cognitive Science and Human Experience. The MIT Press, Cambridge (1991)
Newell, A.: Unified theories of cognition, Coll. “William James lectures 1987”, Cambridge, Mass., Harvard University Press, pp. 17–18 (1990)
Atkinson, R.C., Shiffrin, R.M.: Human memory: a proposed system and its control processes. In: Psychology of Learning and Motivation, vol. 2, pp. 89–195. Academic Press (1968). https://doi.org/10.1016/S0079-7421(08)60422-3
Brasoveanu, Adrian, Dotlačil, Jakub: Computational Cognitive Modeling and Linguistic Theory. LCM, vol. 6. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31846-8
Sun, R.: Desiderata for cognitive architectures. Philos. Psychol. 17(3), 341–373 (2004)
Insaurralde, C.C., Blasch, E.: Uncertainty in avionics analytics ontology for decision-making support. J. Adv. Inf. Fusion 13(2), 255–274 (2019)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 4th edn. Pearson, Boston (2021)
Oliver, W., Marc, H., Nele, R.: ACT-R model for cognitive assistance in handling flight deck alerts. In: International Conference on Cognitive Modelling, Montreal, Canada (2019)
Smart, P.R., Scutt, T., Sycara, K., Shadbolt, N.R.: Integrating ACT-R cognitive models with the Unity game engine. GI Global, Hershey, Pennsylvania, USA (2016)
Courtemanche, M.A., Tato, A., Nkambou, R.: Ontological reference model for piloting procedures. In: Crossley, S., Popescu, E. (eds.) Intelligent Tutoring Systems (ITS 2022). LNCS, vol. 13284, pp. 95–104. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09680-8_9
Ghaderi, M., Courtemanche, M.A., Ben Abdessalem, H., Nkambou, R., Frasson, C.: Attentional tasks model: a focus group approach. In: Krouska, A., Troussas, C., Caro, J. (eds.) Novel and Intelligent Digital Systems: Proceedings of the 2nd International Conference (NiDS 2022). LNNS, vol. 556, pp. 297–307. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-17601-2_29
Estes, S., Burns, K., Helleberg, J., Long, K., Stein, J., Pollack, M.: Digital copilot: cognitive assistance for pilots. In: Proceedings of the AAAI Fall Symposium on Cognitive Assistance in Government and Public Sector Applications (2016)
Oltramari, A., Lebiere, C.: Mechanisms meet content: integrating cognitive architectures and ontologies. In: AAAI Fall Symposium: Advances in Cognitive Systems (2001)
Abrahão, E., Hirakawa, A.R.: Task ontology modeling for technical knowledge representation in agriculture field operations domain. In: Proceedings of the 2017 Second International Conference on Information Systems Engineering (ICISE), pp. 12–16 (2017)
Dehais, F., Roy, R.N., Scannella, S.: Inattentional deafness to auditory alarms: inter-individual differences, electrophysiological signature and single trial classification. Behav. Brain Res. 360, 51–59 (2019). https://doi.org/10.1016/j.bbr.2018.11.045
Zhang, Z., Russwinkel, N., Prezenski, S.: Modeling individual strategies in dynamic decision-making with ACT-R: a task toward decision-making assistance in HCI. Procedia Comput. Sci. 145, 668–674 (2018). https://doi.org/10.1016/j.procs.2018.11.064
Taatgen, N.A.: Cognitive modelling: a new look at individual differences. Dutch J. Psychol. 167–176 (1999)
Kotseruba, I., Tsotsos, J.K.: 40 years of cognitive architectures: core cognitive abilities and practical applications. Artif. Intell. Rev. 53(1), 17–94 (2018). https://doi.org/10.1007/s10462-018-9646-y
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111(4), 1036–1060 (2004)
Raluca, Budiu: About ACT-R (2013). http://act-r.psy.cmu.edu/about/
Google Research: Google colab notebook (2023). https://colab.research.google.com
W3C: OWL 2 web ontology language: structural specification and functional-style syntax (second edition) (2012). https://www.w3.org/TR/owl2-overview
Acknowledgement
We acknowledge the support of CRIAQ, the Natural Sciences and Engineering Research Council of Canada (NSERC), CAE, Bombardier, and BMU.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tchio, G.C.T., Courtemanche, MA., Tato, A.A.N., Nkambou, R., Psyché, V. (2023). Integrating an Ontological Reference Model of Piloting Procedures in ACT-R Cognitive Architecture to Simulate Piloting Tasks. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-32883-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-32882-4
Online ISBN: 978-3-031-32883-1
eBook Packages: Computer ScienceComputer Science (R0)