Invited Speakers

Keisuke Okumura
Cambridge

Abstract: 

Multi-agent pathfinding (MAPF) has been actively studied since the 2010s. While early work focused on developing efficient optimal algorithms, recent research has increasingly shifted toward more practical directions — including suboptimal yet highly scalable MAPF methods aimed at real-time multi-robot fleet control in logistics. So, where do we stand today? This talk will explore these modern foundational algorithms and their extensions, which are almost ready to tackle real-world challenges. The talk also considers some promising, albeit premature, techniques for pushing MAPF research into new frontiers.

Bio: 

Keisuke Okumura is a Researcher at the National Institute of Advanced Industrial Science and Technology (AIST), Japan, and a Visiting Scholar at the University of Cambridge, UK. He received his Ph.D. in Computer Science from Tokyo Institute of Technology in 2023. His research focuses on intelligent collective behaviour of swarm agents, particularly the development of multi-agent planning methods tailored to large-scale automation. His honours include the ICAPS 2022 Best Student Paper Award and Tokyo Tech’s Ph.D. Dissertation Award (2024). 

Stefan Edelkamp
Charles University and Czech Technical University in Prague

Abstract: 

Multi-goal task planning has been an optimization question in OR for a long time. In terms of Multi-Agent Path Finding (MAPF), introducing multiple goals and flexible goal assignments lead to what is called Combinatorial MAPF. Starting with Anonymous MAPF, we will discuss the computational complexity of CMAPF in undirected graphs.

Next, we will present the communication-constrained CMAPF and propose a graph-search based algorithm to address this task. Given a fleet of agents, an environment represented by a weighted graph, and a sequence of goals, the aim is to visit all the goals without breaking the communication constraints between the agents, minimizing completion time. The resulting paths show how the agents can coordinate their individual paths, not only with respect to the next goal, but also with respect to all future goals, all the time keeping the communication within the fleet intact.

While the discrete multi-goal task planning problem is already hard, the "physical" motion planning problem is even more demanding and considered one the most important problems in robotics nowadays. Robots have sizes, heading, and velocity, and their motion can often be described only according to non-linear differential equations. The dynamics of movements, existing obstacles and many waypoints to visit are only some of the challenges to face.

In real-world problems like submarine movements, we often have additional constraints like inspecting areas of interest in some certain order, while still minimizing the time for the travel. The trickiest part is to solve the hard combinatorial discrete tasks, and - at the same time - providing valid trajectories for the robot. We extend a framework in which discrete planning problems are used as a heuristic guidance. In case of inspection, we generate the waypoints fully automatically, using a filtering mechanism based on hitting sets. Other topics of interest are multi-robot multi-goal planning with limits in energy consumption.

As an open problem, we will look at the connection of MAPF to Petri Nets, and to AI Planning. 

Bio: 

Stefan Edelkamp is professor in AI at both Charles University and Czech Technical University in Prague. Previously he was professor at King's College London, leading the planning group. Before that he was working at the Institute for Artificial Intelligence, Faculty of Computer Science and Mathematics of the University of Bremen, and at the University of Applied Science in Darmstadt. He earned his Ph.D. from Freiburg University and led a junior research group at Technical University of Dortmund. His scientific interest is Algorithmic Intelligence, and includes areas such as Heuristic Search, Action Planning, Game Playing, Machine Learning, Motion Planning, Multi-Agent Search and Simulation, Model Checking, Algorithm Engineering, etc. Stefan Edelkamp has organized international conferences, workshops, and seminars and won several performance awards at international planning competitions. Together with Stefan Schroedl he is author of the text book "Heuristic Search - Theory and Applications" published by Morgan Kaufmann / Elsevier Science. His recent textbook is on "Algorithmic Intelligence - Towards an Algorithmic Foundation for Artificial Intelligence" published by Springer Nature.