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...