Advertisement

Simplest Instructions: Finding Easy-to-Describe Routes for Navigation

  • Kai-Florian Richter
  • Matt Duckham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5266)

Abstract

Current applications for wayfinding and navigation assistance usually calculate the route to a destination based on the shortest or fastest path from the origin. However, numerous findings in cognitive science show that the ease of use and communication of route instructions depends on factors other than just the length of a route, such as the number and complexity of decision points. Building on previous work to improve the automatic generation of route instructions, this paper presents an algorithm for finding routes associated with the “simplest” instructions, taking into account fundamental principles of human direction giving, namely decision point complexity, references to landmarks, and spatial chunking. The algorithm presented can be computed in the same order of time complexity as Dijkstra’s shortest path algorithm, O(n 2). Empirical evaluation demonstrates that the algorithm’s performance is comparable to previous work on “simplest paths,” with an average increase of path length of about 10% compared to the shortest path. However, the instructions generated are on average 50% shorter than those for shortest or simplest paths. The conclusions argue that the compactness of the descriptions, in combination with the incorporation of the basic cognitive principles of chunking and landmarks, provides evidence that these instructions are easier to understand.

Keywords

Short Path Decision Point Spatial Cognition Route Direction Simple Path 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tversky, B., Lee, P.U.: Pictorial and verbal tools for conveying routes. In: Freksa, C., Mark, D.M. (eds.) COSIT 1999. LNCS, vol. 1661, pp. 51–64. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Habel, C.: Incremental generation of multimodal route instructions. In: Natural Language Generation in Spoken and Written Dialogue, AAAI Spring Symposium 2003, Palo Alto, CA (2003)Google Scholar
  3. 3.
    Dale, R., Geldof, S., Prost, J.P.: Using natural language generation in automatic route description. Journal of Research and Practice in Information Technology 37(1), 89–105 (2005)Google Scholar
  4. 4.
    Klippel, A., Tappe, H., Kulik, L., Lee, P.U.: Wayfinding choremes — a language for modeling conceptual route knowledge. Journal of Visual Languages and Computing 16(4), 311–329 (2005)CrossRefGoogle Scholar
  5. 5.
    Dijkstra, E.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Denis, M.: The description of routes: A cognitive approach to the production of spatial discourse. Cahiers Psychologie Cognitive 16(4), 409–458 (1997)Google Scholar
  7. 7.
    Lovelace, K.L., Hegarty, M., Montello, D.R.: Elements of good route directions in familiar and unfamiliar environments. In: Freksa, C., Mark, D.M. (eds.) COSIT 1999. LNCS, vol. 1661, pp. 65–82. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Klippel, A., Tappe, H., Habel, C.: Pictorial representations of routes: Chunking route segments during comprehension. In: Freksa, C., Brauer, W., Habel, C., Wender, K.F. (eds.) Spatial Cognition III. LNCS (LNAI), vol. 2685, pp. 11–33. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Hirtle, S.C., Jonides, J.: Evidence of hierarchies in cognitive maps. Memory & Cognition 13(3), 208–217 (1985)Google Scholar
  10. 10.
    Couclelis, H., Golledge, R.G., Gale, N., Tobler, W.: Exploring the anchor-point hypothesis of spatial cognition. Journal of Environmental Psychology 7, 99–122 (1987)CrossRefGoogle Scholar
  11. 11.
    Michon, P.-E., Denis, M.: When and why are visual landmarks used in giving directions? In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 400–414. Springer, Heidelberg (2001)Google Scholar
  12. 12.
    Raubal, M., Winter, S.: Enriching wayfinding instructions with local landmarks. In: Egenhofer, M., Mark, D. (eds.) GIScience 2002. LNCS, vol. 2478, pp. 243–259. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Elias, B.: Extracting landmarks with data mining methods. In: Kuhn, W., Worboys, M., Timpf, S. (eds.) COSIT 2003. LNCS, vol. 2825, pp. 375–389. Springer, Heidelberg (2003)Google Scholar
  14. 14.
    Caduff, D., Timpf, S.: The landmark spider: Representing landmark knowledge for wayfinding tasks. In: Barkowsky, T., Freksa, C., Hegarty, M., Lowe, R. (eds.) Reasoning with mental and external diagrams: computational modeling and spatial assistance - Papers from the 2005 AAAI Spring Symposium, Menlo Park, CA, pp. 30–35 (2005)Google Scholar
  15. 15.
    Hansen, S., Richter, K.F., Klippel, A.: Landmarks in OpenLS - a data structure for cognitive ergonomic route directions. In: Raubal, M., Miller, H., Frank, A.U., Goodchild, M.F. (eds.) GIScience 2006. LNCS, vol. 4197, pp. 128–144. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Duckham, M., Kulik, L.: ”Simplest” paths: Automated route selection for navigation. In: Kuhn, W., Worboys, M., Timpf, S. (eds.) COSIT 2003. LNCS, vol. 2825, pp. 169–185. Springer, Heidelberg (2003)Google Scholar
  17. 17.
    Richter, K.-F., Klippel, A.: A model for context-specific route directions. In: Freksa, C., Knauff, M., Krieg-Brückner, B., Nebel, B., Barkowsky, T. (eds.) Spatial Cognition IV. LNCS (LNAI), vol. 3343, pp. 58–78. Springer, Heidelberg (2005)Google Scholar
  18. 18.
    Mark, D.: Automated route selection for navigation. IEEE Aerospace and Electronic Systems Magazine 1, 2–5 (1986)CrossRefGoogle Scholar
  19. 19.
    Richter, K.F.: A uniform handling of different landmark types in route directions. In: Winter, S., Duckham, M., Kulik, L., Kuipers, B. (eds.) COSIT 2007. LNCS, vol. 4736, pp. 373–389. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Winter, S.: Weighting the path continuation in route planning. In: GIS 2001: Proceedings of the 9th ACM international symposium on Advances in geographic information systems, pp. 173–176. ACM, New York (2001)CrossRefGoogle Scholar
  21. 21.
    Haque, S., Kulik, L., Klippel, A.: Algorithms for reliable navigation and wayfinding. In: Barkowsky, T., Knauff, M., Ligozat, G., Montello, D.R. (eds.) Spatial Cognition 2007. LNCS (LNAI), vol. 4387, pp. 308–326. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kai-Florian Richter
    • 1
  • Matt Duckham
    • 2
  1. 1.Transregional Collaborative Research Center SFB/TR 8 Spatial CognitionUniversität BremenGermany
  2. 2.Department of GeomaticsThe University of MelbourneAustralia

Personalised recommendations