Skip to main content

Advertisement

SpringerLink
Log in

Tailoring a cognitive model for situation awareness using machine learning

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Using a pure machine learning approach to enable the generation of behavior for agents in serious gaming applications can be problematic, because such applications often require human-like behavior for agents that interact with human players. Such human-like behavior is not guaranteed with e.g. basic reinforcement learning schemes. Cognitive models can be very useful to establish human-like behavior in an agent. However, they require ample domain knowledge that might be difficult to obtain. In this paper, a cognitive model is taken as a basis, and the addition of scenario specific information is for a large part automated by means of machine learning techniques. The performance of the approach of automatically adding scenario specific information is rigorously evaluated using a case study in the domain of fighter air combat. An evolutionary algorithm is proposed for automatically tailoring a cognitive model for situation awareness of fighter pilots. The standard algorithm and several extensions are evaluated with respect to performance in air combat. The results show that it is possible to apply the algorithm to optimize belief networks for cognitive models of intelligent agents (adversarial fighters) in the aforementioned domain, thereby reducing the effort required to elicit knowledge from experts, while retaining the required ‘human-like’ behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abt C (1970) Serious Games. The Viking Press, New York

    Google Scholar 

  2. Hoogendoorn M, Lambalgen RM van, Treur J (2011) Modeling situation awareness in human-like agents using mental models. In: Walsh T (ed) Proceedings of the twenty-second international joint conference on artificial intelligence, IJCAI’11, pp 1697–1704

  3. Harbers M, Bosch KVD, Meyer JJ (2009) Modeling agent with a theory of mind. In: Baeza-Yates R, Lang J, Mitra S, Parsons S, Pasi G (eds) Proceedings of the 2009 IEEE/WIC/ACM international joint conference on web intelligence and agent technology. IEEE Computer Society Press, pp 217–224

  4. Hoogendoorn M, Merk RJ, Treur J (2010) An agent model for decision making based upon experiences applied in the domain of fighter pilots. In: Huang X J, Ghorbani A A, Hacid M S, Yamaguchi T (eds) Proceedings of the 10th IEEE/WIC/ACM international conference on intelligent agent technology IAT’10. IEEE Computer Society Press, 2010, pp 101–108

  5. Gini ML, Hoogendoorn M, Lambalgen RM van (2011) Learning belief connections in a model for situation awareness. In: Kinny D, Hsu D (eds) Proceedings of the 14th international conference on principles and practice of multi-agent systems, PRIMA’11. Lecture notes in artificial intelligence. Springer Verlag

  6. Bosse T, Merk RJ, Treur J (2012) Modelling temporal aspects of situation awareness. In: Huang T et al (eds) Proceedings of the 19th international conference on neural information processing, ICONIP’12, Part I. Lecture notes in computer science, vol 7663. Springer-Verlag, Berlin Heidelberg, p 2012

    Google Scholar 

  7. Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1):32–64

    Article  Google Scholar 

  8. Baluja S (1994) Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. CMU-CS-94-163, Carnegie Mellon University

  9. Salerno J, Hinman M, Boulware D, Bello P (2003) Information fusion for situational awareness. Proc. Int’l Conf. on International Fusion Cairns, Australia

  10. Dearden R, Willeke T, Simmons R, Verma V, Hutter F, Thrun S (2004) Real-time fault detection and situational awareness for rovers: report on the Mars technology program task, Proc. Aerospace Conference, vol 2, pp 826–840, March 2004

  11. Brannon N, Conrad G, Draelos T, Seiffertt J, Wunsch D, Zhang P (2009) Coordinated machine learning and decision support for situation awareness. Neural Netw 22(3):316–325

    Article  Google Scholar 

  12. Laird J, Rosenbloom P, Newell A (1987) Soar: An Architecture for General Intelligence. Artif Intell 33:1–64

    Article  Google Scholar 

  13. Anderson JR (1984) The architecture of cognition. Harvard University Press, Cambridge

    Google Scholar 

  14. Kieras DE, Meyer DE (1997) An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-computer interaction, pp 391–438

  15. Naveh I, Sun R (2006) A cognitively based simulation of academic science. Comput Mqth Organ Th 313–337

  16. Smith RE, Dike BA, Mehra RK (2000) Classier systems in combat: Two-sided learning of maneuvers for advanced fighter aircraft. Comput Methods Appl Mech Eng 186(2–4):421–437

    Article  MATH  Google Scholar 

  17. Stanley K, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evol Comput 10(2):99–127

    Article  Google Scholar 

  18. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Co. Inc., Redwood City

    MATH  Google Scholar 

  19. Merk R (2010) A computational model on surprise and its effects on agent behaviour in simulated environments. In: Advances in practical applications of agents and multiagent systems (PAAMS), 2010

  20. Jones RM, Laird JE, Nielsen PE, Coulter KJ, Kenny P, Koss FV (1999) Automated intelligent, pilots for combat flight simulation. AI magazine, volume 20, number 1, 1999 American Association for Artificial Intelligence, pp 27–41

  21. Spronck P, Ponsen M, Sprinkhuizen-Kuyper I, Postma E (2006) Adaptive game AI with dynamic scripting. Mach Learn 63(3):217–248. doi:10.1007/s10994-006-6205-6

    Article  Google Scholar 

  22. Toubman A, Roessingh JJM, Spronck P, Plaat A, Herik HJ van den (2014) Dynamic Scripting with Team Coordination in Air Combat Simulation. Paper presented at the 27th IEA-AIE 2014. Proceedings of the 27th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, LNCS, Springer Verlag

  23. Koopmanschap R (2012) Improving a model for situation awareness in CGF fighters with machine learning techniques, master thesis. VU University Amsterdam, p 2012

  24. Simon HA (1992) What is an “explanation” of behavior? Psychol Sci 3(3):150–161

    Article  Google Scholar 

Download references

Acknowledgments

Lt. Col. Roel Rijken (Royal Netherlands Air Force) created a first version of the Off-Line Learning Environment (OLLE) used in this study and provided valuable advice. The authors also thank Robbert-Jan Merk, Remco Meiland and Pieter Huibers at the National Aerospace Laboratory NLR for their feedback and assistance with the simulation environment. Finally, the authors thank two anonymous reviewers for carefully scrutinizing the study. This research has greatly benefitted from their help.

Author information

Authors and Affiliations

  1. National Aerospace Laboratory, Department of Training, Simulation, and Operator Performance, Anthony Fokkerweg 2, 1059, CM, Amsterdam, the Netherlands

    Richard Koopmanschap & Jan Joris Roessingh

  2. Department of Computer Science, VU University Amsterdam, De Boelelaan 1081, 1081, HV, Amsterdam, the Netherlands

    Richard Koopmanschap & Mark Hoogendoorn

Authors
  1. Richard Koopmanschap
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Hoogendoorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Joris Roessingh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richard Koopmanschap.

Additional information

This article is a significantly extended version of the following paper: Koopmanschap, R. Hoogendoorn, M., Roessingh, J.J., Learning Parameters for a Cognitive Model on Situation Awareness. Ali, M., Bosse, T., Hindriks. K.V., Hoogendoorn, M., Jonker, C.M., and Treur, J. (eds.), Recent Trends in Applied Artificial Intelligence, Proceedings of the 26th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, IEA-AIE 2013, Springer, LNCS 7906, 2013, pp. 22-32.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koopmanschap, R., Hoogendoorn, M. & Roessingh, J.J. Tailoring a cognitive model for situation awareness using machine learning. Appl Intell 42, 36–48 (2015). https://doi.org/10.1007/s10489-014-0584-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-014-0584-3

Keywords

Search

Navigation