Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
AI Magazine
Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tractable multi-agent path planning on grid maps
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
MAPP: a scalable multi-agent path planning algorithm with tractability and completeness guarantees
Journal of Artificial Intelligence Research
Complete algorithms for cooperative pathfinding problems
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
Artificial Intelligence
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In the multi-agent pathfinding problem, groups of agents need to plan paths between their respective start and goal locations in a given environment, usually a two-dimensional map. Existing approaches to this problem include using static or dynamic information to help coordination. However, the resulting behaviour is not always desirable, in that too much information is hand-coded into the problem, agents take paths which look unintelligent, or because the agents collide and must re-plan frequently. We present a distributed approach in which agents share information about the direction in which they traveled when passing through each location. This information is then used to encourage agents passing through the same location to travel in the same direction as previous agents. In addition to this new approach, we present performance metrics for multi-agent path planning as well as experimental results for the new approach. These results indicate that the number of collisions between agents is reduced and that the visual fidelity is improved.