Theoretical Computer Science
The Canadian Traveller Problem
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Improved results for route planning in stochastic transportation
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
New lower bounds for oblivious routing in undirected graphs
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Canadian traveler problem with remote sensing
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Estimating end-to-end delays under changing conditions
Proceedings of the 8th ACM MobiCom workshop on Challenged networks
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The Canadian Traveller problem is a stochastic shortest paths problem in which one learns the cost of an edge only when arriving at one of its endpoints. The goal is to find an optimal policy that minimizes the expected cost of travel. The problem is known to be #P-hard. Since there has been no significant progress on approximation algorithms for several decades, we have chosen to seek out special cases for which exact solutions exist, in the hope of demonstrating techniques that could lead to further progress. Applying a mix of techniques from algorithm analysis and the theory of Markov Decision Processes, we provide efficient exact algorithms for directed acyclic graphs and (undirected) graphs of disjoint paths from source to destination with random two-valued edge costs. We also give worst-case performance analysis and experimental data for two natural heuristics.