Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Eighteenth national conference on Artificial intelligence
Performance bounds for planning in unknown terrain
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning in real-time search: a unifying framework
Journal of Artificial Intelligence Research
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Fast replanning for navigation in unknown terrain
IEEE Transactions on Robotics
Real-time heuristic search with depression avoidance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Heuristic search when time matters
Journal of Artificial Intelligence Research
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In this paper, we investigate real-time path planning in static terrain, as needed in video games. We introduce the game time model, where time is partitioned into uniform time intervals, an agent can execute one movement during each time interval, and search and movements are done in parallel. The objective is to move the agent from its start location to its goal location in as few time intervals as possible. For known terrain, we show experimentally that Time-Bounded A* (TBA*), an existing real-time search algorithm for undirected terrain, needs fewer time intervals than two state-of-the-art real-time search algorithms and about the same number of time intervals as A*. TBA*, however, cannot be used when the terrain is not known initially. For initially partially or completely unknown terrain, we thus propose a new search algorithm. Our Time-Bounded Adaptive A* (TBAA*) extends TBA* to on-line path planning with the freespace assumption by combining it with Adaptive A*. We prove that TBAA* either moves the agent from its start location to its goal location or detects that this is impossible - an important property since many existing realtime search algorithms are not able to detect efficiently that no path exists. Furthermore, TBAA* can eventually move the agent on a cost-minimal path from its start location to its goal location if it resets the agent into its start location whenever it reaches its goal location. We then show experimentally in initially partially or completely unknown terrain that TBAA* needs fewer time intervals than several state-of-the-art complete and real-time search algorithms and about the same number of time intervals as the best compared complete search algorithm, even though it has the advantage over complete search algorithms that the agent starts to move right away.