报告题目：Progress on Multi-Agent Path Finding in Real-World Scenarios
日期：2018年1 月8日 (周一)
Abstract: —Teams of agents must often assign target locations among themselves and then plan collision-free paths to their target locations. Examples include autonomous aircraft towing vehicles, automated warehouse systems, office robots, and game characters in video games. For example, soon, autonomous aircraft towing vehicles will tow aircraft all the way from the runways to their gates (and vice versa), thereby reducing pollution, energy consumption, congestion, and human workload. Today, hundreds of robots already navigate autonomously in Amazon fulfillment centers to move inventory pods all the way from their storage locations to the inventory stations that need the products they store (and vice versa). Path planning for these robots is NP-hard, yet one must find high-quality collision-free paths for them in real-time. Shorter paths result in higher throughput or lower operating costs (since fewer robots are required). In this talk, I describe several versions of multi-agent pathfinding (MAPF) problems, their complexities, algorithms for solving them, and their applications. I also present a hierarchical planning architecture that combines ideas from artificial intelligence and robotics. This research is joint research with N. Ayanian, L. Cohen, W. Hoenig, S. Koenig, S. Kumar, J. Li, G. Sharon, C. Tovey, T. Uras, H. Xu, and other researchers and students.
Hang Ma is a Ph.D. student in computer science at the University of Southern California advised by Professor Sven Koenig, who is a Fellow of AAAI. He received a B.S. (First Class with Distinction) in Computing Science from Simon Fraser University in 2012, a B.Eng. in Computer Science and Technology from Zhejiang University in 2012, and an M.S. in Computer Science from McGill University in 2014. He has published over 10 papers in top AI conferences/journals, including AAAI, IJCAI, AAMAS, JAIR, etc. He is the recipient of a USC Annenberg Graduate Fellowship. Hang is interested in artificial intelligence, machine learning, and robotics. More information can be found on his homepage (http://www-scf.usc.edu/~hangma/).
Graduate Fellowship. Hang is interested in artificial intelligence, machine learning, and robotics. More information can be found on his homepage (http://www-scf.usc.edu/~hangma/).