Artificial Intelligence
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Comparing real-time and incremental heuristic search for real-time situated agents
Autonomous Agents and Multi-Agent Systems
Partial pathfinding using map abstraction and refinement
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Learning in real-time search: a unifying framework
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Moving target search with intelligence
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Real-time heuristic search with depression avoidance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Avoiding and escaping depressions in real-time heuristic search
Journal of Artificial Intelligence Research
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Heuristic depressions are local minima of heuristic functions. While visiting one them, real-time (RT) search algorithms like LRTA* will update the heuristic value for most of their states several times before escaping, resulting in costly solutions. Existing RT search algorithm tackle this problem by doing more search and/or lookahead but do not guide search towards leaving depressions. We present eLSS-LRTA* a new RT search algorithm based on LSS-LRTA* that actively guides search towards exiting regions with heuristic depressions. We show that our algorithm produces better-quality solutions than LSS-LRTA* for equal values of lookahead in standard RT benchmarks.