Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial Intelligence
Algorithm 360: shortest-path forest with topological ordering [H]
Communications of the ACM
A Comparison of Fast Search Methods for Real-Time Situated Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Partial pathfinding using map abstraction and refinement
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Graph abstraction in real-time heuristic search
Journal of Artificial Intelligence Research
Dynamic control in real-time heuristic search
Journal of Artificial Intelligence Research
Real-time heuristic search with a priority queue
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On learning in agent-centered search
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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
Real-time heuristic search with depression avoidance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Finding patterns in an unknown graph
AI Communications - The Symposium on Combinatorial Search
Avoiding and escaping depressions in real-time heuristic search
Journal of Artificial Intelligence Research
Heuristic search when time matters
Journal of Artificial Intelligence Research
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Real-time heuristic search algorithms are used for planning by agents in situations where a constant-bounded amount of deliberation time is required for each action regardless of the problem size. Such algorithms interleave their planning and execution to ensure real-time response. Furthermore, to guarantee completeness, they typically store improved heuristic estimates for previously expanded states. Although subsequent planning steps can benefit from updated heuristic estimates, many of the same states are expanded over and over again. Here we propose a variant of the A* algorithm, Time-Bounded A* (TBA*), that guarantees real-time response. In the domain of path-finding on videogame maps TBA* expands an order of magnitude fewer states than traditional real-time search algorithms, while finding paths of comparable quality. It reaches the same level of performance as recent state-of-the-art real-time search algorithms but, unlike these, requires neither state-space abstractions nor pre-computed pattern databases.