The complexity of Markov decision processes
Mathematics of Operations Research
Elements of Good Route Directions in Familiar and Unfamiliar Environments
COSIT '99 Proceedings of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science
Simplest Instructions: Finding Easy-to-Describe Routes for Navigation
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Linguistic and nonlinguistic turn direction concepts
COSIT'07 Proceedings of the 8th international conference on Spatial information theory
Algorithms for reliable navigation and wayfinding
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
Including landmarks in routing instructions
Journal of Location Based Services
A Qualitative Representation of Route Networks
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Generating adaptive route instructions using hierarchical reinforcement learning
SC'10 Proceedings of the 7th international conference on Spatial cognition
On qualitative route descriptions: representation and computational complexity
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Flexible route guidance through turn instruction graphs
Proceedings of the 1st ACM SIGSPATIAL International Workshop on MapInteraction
Interactive cartographic route descriptions
Geoinformatica
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Route navigation is a widely studied subject from both cognitive and practical points of view. A particular aspect is the generation of (verbal) route instructions that are robust with respect to ambiguous verbal terms. Work in this area usually builds on counting the number of ambiguous turn options along a route. Simple graph search can then be used to derive a route whose description is the most fault-tolerant according to this measure. In this paper we contrast this approach with a probabilistic planning one that estimates the probability of reaching the destination given a probabilistic model of an agent interpreting the route instruction. To this end, we discuss different models of agents, the evaluation of route instructions and derive optimal and approximate approaches for the planning problem.