2022-06 Paper accepted to IROS 2022! Congratualations to Qinghong!
2022-06 A Current Robotics Review article on Graph-Based Multi-Robot Path Finding and Planning is available!
2022-01 Paper accepted to ICRA 2022! Congratualations to Xinyi!
2021-08 Honored to receive funding from the Canada Foundation for Innovation's (CFI) John R. Evans Leaders Fund (JELF) to work on motion-coordination and human-robot interaction problems! Thank you, CFI! Read SFU FAS news and SFU CS news.
2021-08 Honored to receive an ICAPS Best Dissertation Award for my dissertation!
2021-08 Our demo on winning the 2020 Flatland Challenge won a ICAPS 2021 People's Choice Best System Demonstration Award! Our demo video is lots of fun!
2021-08 An Artificial Intelligence Journal paper on symmetry reasoning for Multi-Agent Path Finding is available!
2021-03 Honored to receive an IFAAMAS Victor Lesser Distinguished Dissertation Award Runner-Up for my dissertation!
2021-03 Paper accepted to ICRA 2021!
2021-02 Two papers accepted to ICAPS 2021!
2021-02 Honored to be featured in AAAI New Faculty Highlights! Watch my talk here.
2020-12 Advised Team An_Old_Driver won both Round 1 and Round 2 of the NeurIPS-20 Flatland Challenge (see our ICAPS paper for technical details)!


I am an Assistant Professor in Computing Science at Simon Fraser University and director of the Autonomous Intelligence and Robotics (AIRob) lab.

My interests are mainly in artificial intelligence, robotics, and machine learning. Specifically, I am interested in topics on automated planning, multi-agent/robot systems, spatio-temporal and constraint reasoning, and applications of probabilistic methods and other topics related to graphs, combinatorial optimization, and algorithms.

Research Opportunities

photo by TourismVancouver

I am always looking for self-motivated students at all levels. See my research highlights here or watch my AAAI-21 New Faculty Highlights talk below to learn more about my research.

If you are interested in working with me on AI, robotics, and multi-agent/robot systems, please mention my name in your application to the SFU CS graduate program. Applicants should also refer to the SFU CS graduate program page for more information on the admission requirements and application deadlines.

The SFU main campus is located on the Burnaby Mountain, 12 miles from downtown Vancouver.

Recent Service

Conference and Workshop Organization

Conference Area Chair and (Senior) Program Committee Member

  • AAAI Conference on Artificial Intelligence (AAAI) 2022, 2021, 2020
  • International Joint Conference on Artificial Intelligence (IJCAI) 2022, 2021 (SPC), 2020, 2019
  • International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2021, 2020, 2019
  • International Conference on Automated Planning and Scheduling (ICAPS) 2022, 2021
  • International Conference of the Florida Artificial Intelligence Research Society (FLAIR) 2022, 2021
  • ACM/SIGGRAPH conference on Motion, Interaction and Games (MIG) 2021
  • International Symposium on Multi-Robot and Multi-Agent Systems (MRS) 2021 (AC)
  • International Symposium on Combinatorial Search (SoCS) 2022, 2020

Journal Editing



  • 2014 to 2019, Ph.D. Computer Science, University of Southern California
  • 2012 to 2014, M.Sc. Computer Science, McGill University
  • 2010 to 2012, B.Sc. (First Class with Distinction) Computing Science, Simon Fraser University
  • 2008 to 2010, B.Eng. Computer Science and Technology, Zhejiang University


Recent Publications

  • @inproceedings{ZhengICAPS23,
     author = {Yi Zheng and Hang Ma and Sven Koenig and Erik Kline and T. K. Satish Kumar},
     booktitle = {International Conference on Automated Planning and Scheduling},
     pages = {(in print)},
     title = {Priority-Based Search for the Virtual Network Embedding Problem},
     year = {2023}

  • This article summarizes the New Faculty Highlights talk with the same title at AAAI 2021. Intelligent agents such as different types of robots will soon become an integral part of our daily lives. In real-world multi-agent systems, the most fundamental challenges are assigning tasks to multiple agents (task-level coordination problems) and planning collision-free paths for the agents to task locations (motion-level coordination problems). This article surveys four directions of our research on using intelligent planning techniques for the above multi-agent coordination problems.
     author = {Hang Ma},
     journal = {AI Magazine},
     number = {4},
     pages = {376--382},
     title = {Intelligent Planning for Large-Scale Multi-Agent Systems},
     volume = {43},
     year = {2022}

  • In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where agents constantly engage with new tasks and need to plan collision-free paths to execute them. To execute a task, an agent needs to visit a pair of goal locations, consisting of a pickup location and a delivery location. We propose two variants of an algorithm that assigns a sequence of tasks to each agent using the anytime algorithm Large Neighborhood Search (LNS) and plans paths using the Multi-Agent Path Finding (MAPF) algorithm Priority-Based Search (PBS). LNS-PBS is complete for well-formed MAPD instances, a realistic subclass of MAPD instances, and empirically more effective than the existing complete MAPD algorithm CENTRAL. LNS-wPBS provides no completeness guarantee but is empirically more efficient and stable than LNS-PBS. It scales to thousands of agents and thousands of tasks in a large warehouse and is empirically more effective than the existing scalable MAPD algorithm HBH+MLA*. LNS-PBS and LNS-wPBS also apply to a more general variant of MAPD, namely the Multi-Goal MAPD (MG-MAPD) problem, where tasks can have different numbers of goal locations.
     author = {Qinghong Xu and Jiaoyang Li and Sven Koenig and Hang Ma},
     booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and System},
     pages = {in press},
     title = {Multi-Goal Multi-Agent Pickup and Delivery},
     year = {2022}

  • Purpose of Review Planning collision-free paths for multiple robots is important for real-world multi-robot systems and has been studied as an optimization problem on graphs, called multi-agent path finding (MAPF). This review surveys different categories of classic and state-of-the-art MAPF algorithms and different research attempts to tackle the challenges of generalizing MAPF techniques to real-world scenarios. Recent Findings Solving MAPF problems optimally is computationally challenging. Recent advances have resulted in MAPF algorithms that can compute collision-free paths for hundreds of robots and thousands of navigation tasks in seconds of runtime. Many variants of MAPF have been formalized to adapt MAPF techniques to different real-world requirements, such as considerations of robot kinematics, online optimization for real-time systems, and the integration of task assignment and path planning. Summary Algorithmic techniques for MAPF problems have addressed important aspects of several multi-robot applications, including automated warehouse fulfillment and sortation, automated train scheduling, and navigation of non-holonomic robots and quadcopters. This showcases their potential for real-world applications of large-scale multi-robot systems.
     author = {Hang Ma},
     booktitle = {Current Robotics Reports},
     pages = {1--8},
     title = {Graph-Based Multi-Robot Path Finding and Planning},
     year = {2022}

  • We formalize and study the multi-goal task assignment and pathfinding (MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task consists of a sequence of goal locations, and collision-free paths for the agents that visit all goal locations of their assigned tasks in sequence. Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally. We present algorithms that build upon algorithmic techniques for the multi-agent pathfinding problem and solve the MG-TAPF problem optimally and bounded-suboptimally. We experimentally compare these algorithms on a variety of different benchmark domains.
     author = {Xinyi Zhong and Jiaoyang Li and Sven Koenig and Hang Ma},
     booktitle = {IEEE International Conference on Robotics and Automation},
     pages = {10731--10737},
     title = {Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding},
     year = {2022}

  • Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of the reasons why MAPF is so hard to solve is due to a phenomenon called pairwise symmetry, which occurs when two agents have many different paths to their target locations, all of which appear promising, but every combination of them results in a collision. We identify several classes of pairwise symmetries and show that each one arises commonly in practice and can produce an exponential explosion in the space of possible collision resolutions, leading to unacceptable runtimes for current state-of-the-art (bounded-sub)optimal MAPF algorithms. We propose a variety of reasoning techniques that detect the symmetries efficiently as they arise and resolve them by using specialized constraints to eliminate all permutations of pairwise colliding paths in a single branching step. We implement these ideas in the context of a leading optimal MAPF algorithm CBS and show that the addition of the symmetry reasoning techniques can have a dramatic positive effect on its performance — we report a reduction in the number of node expansions by up to four orders of magnitude and an increase in scalability by up to thirty times. These gains allow us to solve to optimality a variety of challenging MAPF instances previously considered out of reach for CBS.
     author = {Jiaoyang Li and Daniel Harabor and Peter J. Stuckey and Hang Ma and Graeme Gange and Sven Koenig},
     journal = {Artifical Intelligence},
     pages = {103574},
     title = {Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search},
     volume = {301},
     year = {2021}

  • Full list of publications