Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Speeding up problem solving by abstraction: a graph oriented approach
Artificial Intelligence - Special volume on empirical methods
Performance bounds for planning in unknown terrain
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Artificial Intelligence
Incremental heuristic search in AI
AI Magazine
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
The fringe-saving A* search algorithm: a feasibility study
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Fast replanning for navigation in unknown terrain
IEEE Transactions on Robotics
A survey and classification of A* based best-first heuristic search algorithms
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Physical search problems with probabilistic knowledge
Artificial Intelligence
Fast Path Re-planning Based on Fast Marching and Level Sets
Journal of Intelligent and Robotic Systems
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Fringe-Saving A* is an incremental version of A* that repeatedly finds shortest paths from a fixed start cell to a fixed goal cell in a known gridworld in case the traversability of cells changes over time. It restores the content of the OPEN and CLOSED lists of A* at the point in time when an A* search for the current search problem could deviate from the A* search for the previous search problem. Thus, Fringe-Saving A* reuses the beginning of the previous A* search that is identical to the current A* search. In this paper, we generalize the correctness proof of Fringe-Saving A* to cover the case where the goal cell changes over time in addition to the traversability of cells. We then apply Fringe-Saving A* to the problem of moving an agent along a shortest path from its current cell to a fixed destination cell in a known gridworld, where the shortest path is replanned whenever the traversability of cells changes. Our experimental results show that the resulting Dynamic Fringe-Saving A* algorithm can outperform both repeated A* searches and D* Lite (a state-of-the-art incremental version of A*) in highly dynamic gridworlds, with runtime savings of up to a factor of about 2.5.