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A Probabilistic Framework for Object Descriptions in Indoor Route Instructions

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Spatial Information Theory (COSIT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8116))

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Abstract

Automatically generated route instructions are common in modern cars, but for indoor environments, such systems hardly exist, possibly due to the fundamental difference of street networks and indoor environments. Indoor environments often lack the clear decision points present in street networks and involve a spatially complex layout. Therefore, good route instructions for indoor environments should provide context by relating instructions to environmental features—they need to describe and locate objects. This paper is concerned with automatic generation of in-advance route instructions for indoor scenarios. Motivated by empirical research, we propose a probabilistic framework for generating referring expressions using vague properties and graded models of spatial relations. We discuss the relevance of this framework for generating indoor route instructions and demonstrate the appropriateness of this approach in case studies.

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Authors and Affiliations

  1. SFB/TR 8 Spatial Cognition, University of Bremen, Germany

    Vivien Mast & Diedrich Wolter

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  1. Vivien Mast
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  2. Diedrich Wolter
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Editor information

Editors and Affiliations

  1. School of Linguistics & English Language, Bangor University, College Road, LL57 2DG, Bangor, UK

    Thora Tenbrink

  2. School of Computing, University of Leeds, Woodhouse Lane, LS2 9JT, Leeds, UK

    John Stell

  3. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, EX4 4QF, Exeter, UK

    Antony Galton  & Zena Wood  & 

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Mast, V., Wolter, D. (2013). A Probabilistic Framework for Object Descriptions in Indoor Route Instructions. In: Tenbrink, T., Stell, J., Galton, A., Wood, Z. (eds) Spatial Information Theory. COSIT 2013. Lecture Notes in Computer Science, vol 8116. Springer, Cham. https://doi.org/10.1007/978-3-319-01790-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-01790-7_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01789-1

  • Online ISBN: 978-3-319-01790-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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