Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Performance of linear-space search algorithms
Artificial Intelligence
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Distributed navigation in an unknown physical environment
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A new approach to cooperative pathfinding
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Simple optimization techniques for A*-based search
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Journal of Artificial Intelligence Research
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IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Understanding planning tasks: domain complexity and heuristic decomposition
Understanding planning tasks: domain complexity and heuristic decomposition
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AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
The increasing cost tree search for optimal multi-agent pathfinding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Complete algorithms for cooperative pathfinding problems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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We address the problem of optimal pathfinding for multiple agents. Given a start state and a goal state for each of the agents, the task is to find minimal paths for the different agents while avoiding collisions. Previous work on solving this problem optimally, used traditional single-agent search variants of the A* algorithm. We present a novel formalization for this problem which includes a search tree called the increasing cost tree (ICT) and a corresponding search algorithm, called the increasing cost tree search (ICTS) that finds optimal solutions. ICTS is a two-level search algorithm. The high-level phase of ICTS searches the increasing cost tree for a set of costs (cost per agent). The low-level phase of ICTS searches for a valid path for every agent that is constrained to have the same cost as given by the high-level phase. We analyze this new formalization, compare it to the A* search formalization and provide the pros and cons of each. Following, we show how the unique formalization of ICTS allows even further pruning of the state space by grouping small sets of agents and identifying unsolvable combinations of costs. Experimental results on various domains show the benefits and limitations of our new approach. A speedup of up to 3 orders of magnitude was obtained in some cases.