Canadian traveler problem with remote sensing

Authors:
Zahy Bnaya;Ariel Felner;Solomon Eyal Shimony
Affiliations:
Information Systems Engineering, Deutsche Telekom Labs, Ben-Gurion University, Be'er-Sheva, Israel;Information Systems Engineering, Deutsche Telekom Labs, Ben-Gurion University, Be'er-Sheva, Israel;Computer Science, Deutsche Telekom Labs, Ben-Gurion University, Be'er-Sheva, Israel
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

Citing 11
Cited 0

Shortest paths without a map

Theoretical Computer Science
The Canadian Traveller Problem

SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Spatial tessellations: concepts and applications of Voronoi diagrams

Spatial tessellations: concepts and applications of Voronoi diagrams
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
D*lite

Eighteenth national conference on Artificial intelligence
Performance bounds for planning in unknown terrain

Artificial Intelligence - special issue on planning with uncertainty and incomplete information
PPCP: efficient probabilistic planning with clear preferences in partially-known environments

AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Indefinite-horizon POMDPs with action-based termination

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Route planning under uncertainty: the Canadian traveller problem

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
PHA*: finding the shortest path with A* in an unknown physical environment

Journal of Artificial Intelligence Research
Prioritizing point-based POMDP solvers

ECML'06 Proceedings of the 17th European conference on Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Canadian Traveler Problem (CTP) is a navigation problem where a graph is initially known, but some edges may be blocked with a known probability. The task is to minimize travel effort of reaching the goal. We generalize CTP to allow for remote sensing actions, now requiring minimization of the sum of the travel cost and the remote sensing cost. Finding optimal policies for both versions is intractable. We provide optimal solutions for special case graphs. We then develop a framework that utilizes heuristics to determine when and where to sense the environment in order to minimize total costs. Several such heuristics, based on the expected total cost are introduced. Empirical evaluations show the benefits of our heuristics and support some of the theoretical results.