Abstract: Multi-agent coordination requires solving planning problems with extremely large branching factors. As the number of agents grows, conventional search methods become increasingly inefficient because explicitly enumerating all successor states is computationally prohibitive. This talk instead explores the paradigm of combinatorial search with generators, which aims to reduce search effort by delaying the generation and exploration of unpromising regions of the search space. We will see its power in applications ranging from logistics-tailored multi-agent pathfinding with thousands of agents to real-world agile and robust multi-robot systems, illustrating how a generator-based approach can provide the scaffolding for collective automation.

Bio: Keisuke Okumura is a Researcher at the National Institute of Advanced Industrial Science and Technology (AIST), Japan. He received his PhD in Computer Science from the Tokyo Institute of Technology in 2023, and subsequently worked as a Visiting Postdoctoral Scholar at the University of Cambridge. 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, Tokyo Tech’s Ph.D. Dissertation Award (2024), and Funai Information Technology Award for Young Researchers (2026).

Abstract: We present alternative approaches to routing and scheduling in Answer Set Programming (ASP), and explore them in the context of Multi-agent Path Finding. The idea is to capture the flow of time in terms of partial orders rather than time steps attached to actions and fluents. This also abolishes the need for fixed upper bounds on the length of plans. The trade-off for this avoidance is that (parts of) temporal trajectories must be acyclic, since multiple occurrences of the same action or fluent cannot be distinguished anymore. While this approach provides an interesting alternative for modeling routing, it is without alternative for scheduling since fine-grained timings cannot be represented in ASP in a feasible way. This is different for partial orders that can be efficiently handled by external means such as acyclicity and difference constraints. We formally elaborate upon this idea and present several resulting ASP encodings. Finally, we demonstrate their effectiveness via an empirical analysis.

Bio: Torsten Schaub received his diploma and dissertation in informatics in 1990 and 1992, respectively, from the Technical University of Darmstadt, Germany, and his habilitation in informatics in 1995 from the University of Rennes I, France. From 1990 to 1993 he was a research assistant at the Technical University at Darmstadt. From 1993 to 1995, he was a research associate at IRISA/INRIA at Rennes. In 1995 he became University Professor at the University of Angers. Since 1997, he is University Professor for knowledge processing and information systems at the University of Potsdam. From 2014 to 2019, Torsten Schaub held an Inria International Chair at Inria Rennes - Bretagne Atlantique. Torsten Schaub has become a fellow of the European Association for Artificial Intelligence EurAI in 2012. From 2014 to 2019 he served as President of the Association of Logic Programming and was program (co-)chair of LPNMR'09, ICLP'10, ECAI'14, and KR'25. The research interests of Torsten Schaub range from the theoretic foundations to the practical implementation of reasoning from incomplete, inconsistent, and evolving information. His particular research focus lies on Answer set programming and materializes at potassco.org, the home of the open source project Potassco bundling software for Answer Set Programming developed at the University of Potsdam. Last but not least, Torsten Schaub is managing and scientific director at Potassco Solutions GmbH.