Probabilistic Focal Search (PFS) augments traditional Focal Search by probabilistically choosing between the standard heuristic-guided expansion and expanding the minimum‑f node in OPEN. This strategy advances the lower bound, enlarges the FOCAL frontier, and can dramatically reduce node expansions—up to 90% in some benchmarks such as N‑Puzzle and TSP—especially when long f_min plateaus delay useful FOCAL admissions. An anytime variant, APFS, outperforms other tested anytime algorithms on the Generalized Covering TSP, and the same probabilistic scheduler transfers to Dynamic Potential Search as Probabilistic Dynamic Potential Search (PDPS), though its effectiveness varies by domain and bound.
By Minh Vu Duc, Trung Le Huu, H\`a Minh Ho\`ang, Trung Thanh Nguyen, Phuong Khanh Nguyen, Huynh Thi Thanh Binh
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?
By Jungmin Seo, Jaesik Park
arXiv:2606. 05956v1 Announce Type: new Abstract: Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search.
By Tzur Shubi, Ariel Felner, Solomon Eyal Shimony, Shahaf S. Shperberg
arXiv:2610. 28994v1 Announce Type: cross Abstract: We address the Diverse Traveling Salesman Problem (D-TSP) that seeks a set of $k$ distinct TSP tours.
By Ssu-Yuan Lo, Ching-Kai Wang, Kuan-Lun Chen, Hao-Tsung Yang
The paper introduces the concept of any-start-time planning, addressing scenarios where the set of feasible states or actions changes over time, such as path planning among moving obstacles or train boarding. It presents a new data structure, the compound arrival time function (cATF), which compactly encodes the optimal plan as a function of start time and can be assembled via heuristic graph search. Experiments on the SIPP problem demonstrate that any-start-time algorithms using cATFs outperform traditional replanning approaches on difficult instances.
By Devin Wild Thomas (University of New Hampshire, USA), Solomon Eyal Shimony (Ben-Gurion University of the Negev, Israel), Wheeler Ruml (University of New Hampshire, USA), Erez Karpas (Technion - Israel Institute of Technology, Israel), Shahaf S. Shperberg (Ben-Gurion University of the Negev, Israel), Andrew Coles (King's College London, UK)
Finding policies for Markov Decision Processes (MDPs) is a central problem in areas such as Reinforcement Learning and Operations Research. Here, we have to repeatedly choose an action that should be...
arXiv:2602. 07216v2 Announce Type: replace Abstract: Neural combinatorial optimization (NCO) trains fast heuristics for routing problems, but planners often need more than a single solve: they ask which stop to drop, which transition to preserve, or which subset of stops to remove if a route is infeasible.
By Reuben Narad, L\'eonard Boussioux, Michael Wagner
arXiv:2611.07528v1 Announce Type: new
Abstract: LaCAM is a search-based solver for multi-agent pathfinding (MAPF) that runs a depth-first search over joint configurations, using PIBT to generate succ...
By Stephen Chang, Daniel Harabor
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.
By Andrea Di Nezza, Mihir Patel, Fabio Fagnani, Sara Bernardini
arXiv:2607. 00444v1 Announce Type: cross Abstract: Spatiotemporal motion planning, especially in multi-robot settings, requires robots to reason about collision-free regions that change over time, which is challenging in continuous spaces when feasible regions are transient and geometrically constrained.
By Jingtao Tang, Zining Mao, Lufan Yang, Hang Ma
CANOPY is a multi‑fidelity tree bandit algorithm that learns where a piecewise‑smooth prior holds instead of assuming global smoothness. It uses cheap random‑path probes to certify local aggregation bias and then focuses expensive leaf evaluations on cells where smoothness is violated. The method achieves provable fixed‑budget and regret guarantees that scale with the number of discontinuities, matching smooth‑tree rates when no violations exist and approaching structure‑blind search when violations are dense.
By Michael Jerge, Suman Jana
arXiv:2606. 26399v1 Announce Type: new Abstract: We study certain extremal problems in combinatorial geometry that ask about configurations of points in an $n \times n$ grid that satisfy strict, global geometric constraints.
By Luoning Zhang, Xu Zhuang, Tianhao Wang, Nathan Kaplan