Human-Like Sequential Learning of Escape Routes for Virtual Reality Agents View Full Text


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Article Info

DATE

2019-02-18

AUTHORS

Syed Nasir Danial, Jennifer Smith, Faisal Khan, Brian Veitch

ABSTRACT

The Piper Alpha disaster (1988) witnessed 167 casualties. The offshore safety guidelines developed afterward highlighted the need for effective and regular training to overcome the problems in evacuation procedures. Today, virtual environments are effective training platforms due to high-end audio/visual and interactive capabilities. These virtual environments exploit agents with human-like steering capabilities, but with limited or no capacity to learn routes. This work proposes a sequential route learning methodology for agents that resembles the way people learn routes. The methodology developed here exploits a generalized stochastic Petri-net based route learning model iteratively. The simulated results are compared with the route learning strategies of human participants. The data on human participants were collected by the authors from an earlier study in a virtual environment. The main contribution lies in modeling people’s route learning behavior over the course of successive exposures. It is found that the proposed methodology models human-like sequential route learning if there are no easy detours from the original escape route. Although the model does not accurately capture individual learning strategies for all decision nodes, it can be used as a model of compliant, rule-following training guides for a virtual environment. More... »

PAGES

1-27

References to SciGraph publications

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  • 1996. Stochastic Petri Nets in NONE
  • 2012. Snoopy – A Unifying Petri Net Tool in APPLICATION AND THEORY OF PETRI NETS
  • 1959-12. A note on two problems in connexion with graphs in NUMERISCHE MATHEMATIK
  • 2007-07. Landmarks as beacons and associative cues: Their role in route learning in MEMORY & COGNITION
  • 2016. Human Wayfinding: Integration of Mind and Body in COMMUNITY WAYFINDING: PATHWAYS TO UNDERSTANDING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10694-019-00819-7

    DOI

    http://dx.doi.org/10.1007/s10694-019-00819-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112217455


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