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Tailoring a cognitive model for situation awareness using machine learning

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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.

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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.

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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
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  2. Mark Hoogendoorn
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  3. Jan Joris Roessingh
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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.

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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

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

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