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A Neurologically Inspired Model of the Dynamics of Situation Awareness Under Biased Perception

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Current Approaches in Applied Artificial Intelligence (IEA/AIE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9101))

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Abstract

This paper presents a computational cognitive agent model of Situation Awareness (SA), which is inspired by neurocognitive evidences. The model integrates bottom-up and top-down cognitive processes, related to various cognitive states: perception, desires, attention, intention, awareness, ownership, feeling, and communication. The emphasis is on explaining the cognitive basis for biased perception in SA, which is considered to be the most frequent factor in poor SA (the reason for 76% of poor SA errors), through perceptual load. A model like this will be useful in applications which relay on complex simulations (e.g. aviation domain) that need computational agents to represent human action selection together with cognitive details. The validity of the model is illustrated based on simulations for the aviation domain, focusing on a particular situation where an agent has biased perception.

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Author information

Authors and Affiliations

  1. Agent Systems Research Group, Department of Computer Science, VU University Amsterdam, Amsterdam, The Netherlands

    Dilhan J. Thilakarathne

Authors
  1. Dilhan J. Thilakarathne
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Correspondence to Dilhan J. Thilakarathne .

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Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Moonis Ali

  2. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Young Sig Kwon

  3. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Chang-Hwan Lee

  4. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Juntae Kim

  5. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Yongdai Kim

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Thilakarathne, D.J. (2015). A Neurologically Inspired Model of the Dynamics of Situation Awareness Under Biased Perception. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-19066-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

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