Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Speeding up problem solving by abstraction: a graph oriented approach
Artificial Intelligence - Special volume on empirical methods
Time complexity of iterative-deepening-A
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Insights from the aphasia project: designing technology for and with people who have aphasia
CUU '03 Proceedings of the 2003 conference on Universal usability
The participatory design of a sound and image enhanced daily planner for people with aphasia
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Partial pathfinding using map abstraction and refinement
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
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Pathfinding on large maps is time-consuming. Classical search algorithms such as Dijkstra's and A* algorithms may solve difficult problems in polynomial time. However, in real-world pathfinding examples where the search space increases dramatically, these algorithms are not appropriate. Hierarchical pathfinding algorithms that provide abstract plans of future routing, such as HPA* and PRA*, have been explored by previous researchers based on classical ones. Although the two hierarchical algorithms show improvement in efficiency, they only obtain near optimal solutions. In this paper, we introduce the Hierarchical Shortest Path algorithm (HSP) and a hybrid of the HSP and A* (HSPA*) algorithms, which find optimal solutions in logarithmic time for numerous examples. Our empirical study shows that HSP and HSPA* are superior to the classical algorithms on realistic examples, and our experimental results illustrate the efficiency of the two algorithms. We also demonstrate their applicability by providing an overview of our Route Planner project that applies the two algorithms proposed in this paper.