COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Wednesday Seminars - Department of Computer Science and Technology > Pathfinding for 10k agents
Pathfinding for 10k agentsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Ben Karniely. Path planning for multiple agents is the backbone of many interesting applications, ranging from video games to warehouse automation. The underlying problem is typically formulated as finding collision-free paths on graphs, called multi-agent pathfinding (MAPF). This talk dives deep into an important challenge for MAPF : can we build scalable algorithms, say tailored for 10k agents, while still having nice theoretical guarantees like completeness and optimality? Traditionally, these two aspects have been considered incompatible, given the inherent difficulties that guaranteed algorithms face in finding solutions amidst an increasing number of agents. Starting with vanilla approaches to MAPF like A* search, this talk outlines approaches for this quest. Link to join virtually: https://cam-ac-uk.zoom.us/j/81322468305 A recording of this talk is available at the following link: https://www.cl.cam.ac.uk/seminars/wednesday/video/ This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsCosmology lists European Research Group Horizon Forum: The Cell-Materials InterfaceOther talksLMB Seminar: Pharmacological Adaptation of Proteostasis to Ameliorate Aging-associated Degenerative Diseases Public lecture: Unpackable Shapes and the Reinhardt Problem Updates on Krylov complexity: Modular Hamiltonian evolution and QCD. Quiver Yangians and their applications Mechano-chemical active feedback generates convergence extension in epithelial tissue Animal Movement Monitoring: Equipment Development, Field Experiences, Data Analysis, and Models |