Abstract
The generation of route descriptions is a fundamental task of navigation systems. A particular problem in this context is to identify routes that can easily be described and processed by users. In this work, we present a framework for representing route networks with the qualitative information necessary to evaluate and optimize route descriptions with regard to ambiguities in them. We identify different agent models that differ in how agents are assumed to process route descriptions while navigating through route networks and discuss which agent models can be translated into PDL programs. Further, we analyze the computational complexity of matching route descriptions and paths in route networks in dependency of the agent model. Finally, we empirically evaluate the influence of the agent model on the optimization and the processing of route instructions.
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Acknowledgments
The authors thank the anonymous reviewers for helpful feedback and suggestions. Further, the authors are grateful to Julien Hué and Robert Mattmüller for fruitful discussions. This work was supported by DFG (SFB/TR8 Spatial Cognition, project R4-[LogoSpace]), an ARC Future Fellowship (FT0990811), and a joint Go8/DAAD project (D/08/13855).
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This article is a significantly extended version of the following conference paper: Matthias Westphal, Stefan Wölfl, Bernhard Nebel, and Jochen Renz. On Qualitative Route Descriptions: Representation and Computational Complexity. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 1120-1125. AAAI Press 2011.
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Westphal, M., Wölfl, S., Nebel, B. et al. On Qualitative Route Descriptions. J Philos Logic 44, 177–201 (2015). https://doi.org/10.1007/s10992-014-9333-7
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DOI: https://doi.org/10.1007/s10992-014-9333-7