International Journal of Man-Machine Studies
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Situation Awareness in Intelligent Agents: Foundations for a Theory of Proactive Agent Behavior
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Applications of multi-objective evolutionary algorithms to air operations mission planning
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Modeling situation awareness in human-like agents using mental models
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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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.