arXiv:2601. 04884v3 Announce Type: replace Abstract: Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents.
arXiv:2606. 14582v1 Announce Type: new Abstract: Efficient route optimization play a vital role in ensuring both safety and punctuality in railway operations.
arXiv:2608. 06702v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires generating collision-free paths for large agent fleets under strict real-time constraints.
arXiv:2603. 23405v2 Announce Type: replace-cross Abstract: Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms.
The paper introduces Belief-Aware Multi-Agent Path Finding under Map Uncertainty, addressing the challenge that real-world environments can change unexpectedly. It proposes MAGIC, a framework that uses a Gaussian Markov Random Field and Gaussian Belief Propagation to update a shared belief about traversability online, allowing agents to infer the state of nearby unobserved locations. Experiments on standard MAPF benchmarks show that MAGIC reduces the executed sum of costs on 96.3% of instances, outperforming existing approaches across various planner families and large agent teams.
arXiv:2607. 09755v1 Announce Type: new Abstract: Urban rail fare systems may be non-additive: the fare of a single paid journey from an origin to a destination can differ from the sum of fares over multiple legally separated journey legs.
arXiv:2508. 19186v2 Announce Type: replace-cross Abstract: Reactive obstacle avoidance methods often cause agents to become trapped in local minima, because they can often only reason one step ahead (i.
arXiv:2606. 01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents.
HiRAD is a hierarchical reinforcement learning framework designed for continuous-space routing of large-scale AGV fleets, offering real-time guarantees. It introduces a step-level spatiotemporal representation, separates heading selection from velocity control to shrink the action space, and employs an asynchronous event-driven decision pipeline that reduces inference complexity from O(n²) to O(n) and cuts per-step latency by up to 71%. Experiments on random graphs and two warehouse maps show that HiRAD decreases makespan by 45% to 63% and shortens overall runtime.
arXiv:2608. 07734v1 Announce Type: cross Abstract: Automated warehouses face a fundamental trade-off between maximizing storage density and achieving high retrieval throughput.
arXiv:2609.35965v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require t...
The paper introduces Pivot-and-Station Multi-Agent Path Finding (PS‑MAPF), a variant of MAPF where a subset of agents must visit interchangeable pivots before all agents occupy anonymous stations. It provides a full solvability characterization: every instance on a 2‑edge‑connected graph is solvable, and for arbitrary connected graphs a structural effective‑distance measure relative to unoccupied vertices gives a necessary and sufficient condition. The authors prove that minimizing station‑makespan or station‑flowtime is NP‑hard even with a single pivot, and present three algorithms—a complete baseline, a SAT‑based optimal solver, and Pivot‑Prioritized Planning (PPP), which solves 74‑89% of benchmark instances with significantly lower makespan and flowtime than the baseline.
arXiv:2606. 06618v1 Announce Type: cross Abstract: How can we plan long-horizon routes that reach designated goals, visit required waypoints, and remain short when only short-horizon offline trajectories are available?