Artificial Intelligence
AI Magazine
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
Comparing real-time and incremental heuristic search for real-time situated agents
Autonomous Agents and Multi-Agent Systems
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'05 Proceedings of the 19th international joint conference on Artificial 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
Case-based subgoaling in real-time heuristic search for video game pathfinding
Journal of Artificial Intelligence Research
Escaping heuristic depressions in real-time heuristic search
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Avoiding and escaping depressions in real-time heuristic search
Journal of Artificial Intelligence Research
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Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is exceedingly low compared to the actual cost to reach a solution. Real-time search algorithms easily become trapped in those regions since the heuristic values of states in them may need to be updated multiple times, which results in costly solutions. State-of-theart real-time search algorithms like LSS-LRTA*, LRTA*(k), etc., improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding or escaping depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We apply the principle to LSS-LRTA* producing aLSS-LRTA*, a new real-time search algorithm whose search is guided towards exiting regions with heuristic depressions. We show our algorithm outperforms LSS-LRTA* in standard real-time benchmarks. In addition we prove aLSS-LRTA* has most of the good theoretical properties of LSS-LRTA*.