Learning Belief Connections in a Model for Situation Awareness

  • Maria L. Gini
  • Mark Hoogendoorn
  • Rianne van Lambalgen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)


Situational awareness is critical in many human tasks, especially in cases where humans have to make decisions fast and where the result of their decisions might affect their life. This paper addresses the problem of learning optimal values for the parameters of a situational awareness model. The model is a complex network with nodes connected by links with weights, which connect observations to simple beliefs, such as “there is a contact”, to complex belief, such as “the contact is hostile”, and to future beliefs, such as “it is possible the pilot is being targeted”. The model has been built and validated by human experts in the domain of F16 fighter pilots and is used to study human decision making. Given the complexity of the model, there is a need to learn appropriate weights for the connections, which, in turn, affect the activation levels of the beliefs. We propose the use of a genetic algorithm and of a sensitivity based approach to learn the weights in the model. Extensive experimental results are included.


Genetic Algorithm Fitness Function Mental Model Situation Awareness Partial Fitness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maria L. Gini
    • 1
  • Mark Hoogendoorn
    • 2
  • Rianne van Lambalgen
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUnited States of America
  2. 2.Department of Artificial IntelligenceVU University AmsterdamAmsterdamThe Netherlands

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