The paper "Lose the Order, Keep the Hierarchy: Deordering HTN Plans" adapts two classical plan deordering techniques to the Hierarchical Task Network (HTN) planning framework, extending them to respect hierarchical decomposition constraints. The authors evaluate their methods on the IPC 2023 Partial-Order HTN benchmarks and compare them with Optiplan, an HTN planner that generates partially ordered plans directly. Results show a substantial reduction in ordering constraints, with a smaller but noticeable decrease in critical path length.
By Takudzwa Togarepi, Gaspard Quenard, Damien Pellier, Humbert Fiorino
arXiv:2609.39559v1 Announce Type: new
Abstract: In this work we study the problem of MAPFC, a post-optimization step for Multi-Agent Path Finding (MAPF) plans where we are given a feasible plan produ...
By Oren Salzman
arXiv:2608. 11500v1 Announce Type: cross Abstract: Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approval-based committee elections.
By Nicholas Teh
arXiv:2601. 06542v2 Announce Type: replace-cross Abstract: In this paper, we investigate the Resource-Constrained Project Scheduling Problem (RCPSP) with Time-of-Use (TOU) energy tariffs and machine states, a variant of RCPSP for production scheduling, where energy price is part of the criteria and one highly energy-demanding machine can be in one of the following three states: proc, idle, or off.
By Corentin Juvigny, Anton\'in Nov\'ak, Jan Mand\'ik, Zden\v{e}k Hanz\'alek
arXiv:2605. 27000v2 Announce Type: replace-cross Abstract: Repeated sampling with a verifier is the standard way to allocate test-time compute for code generation, with pass@$K$ as the canonical metric.
By Yilong Li, Suman Banerjee, Tong Che
arXiv:2608. 04398v1 Announce Type: cross Abstract: Robotic planning often involves multiple objectives with complex priority relationships, such as safety, efficiency, and regulatory compliance.
By Omar Muhammetkulyyev, Oren Salzman, Tichakorn Wongpiromsarn
arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.
By Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner
The paper presents a method for automatically generating generalized plans in Lean, along with formal proofs of their completeness for given domain constraints. It introduces a semantic‑preserving conversion from PDDL to Lean and uses an LLM to produce both the plan and its proof, whose correctness is verified by Lean’s kernel. Evaluated on 13 benchmark domains with GPT‑5.6‑Sol, the approach yields complete plans and valid proofs for 12 of them, marking a significant advance in automated generalized‑plan completeness.
By Katharina Stein, Chaahat Jain, J\"org Hoffmann, Alexander Koller
The paper introduces Budget-First Tariff Recommendation (BFTR), an algorithmic framework that offers telecom plans without overcharging by aligning final prices with catalog reference prices. BFTR incorporates eight Budget-First strategies, including two novel hybrid approaches—Recursive Hybrid and Knapsack-First Hybrid— and mathematically proves that a suitable offer exists for any positive budget with zero surcharge for non‑interpolated strategies. Experiments on a Nigerian MTN‑inspired dataset show that all strategies achieve zero overcharging, with Recursive Hybrid delivering optimal customer utility and Piecewise maximizing volume, while maintaining sub‑10 ms execution times.
By Ghislain Dorian Tchuente Mondjo
The paper addresses the challenge of scheduling business process activities when the exact sequence of required tasks is uncertain due to data‑driven decisions made during execution. It proposes framing the problem as a chance‑constrained optimization and introduces two formulations: a decomposed two‑stage approach (planning to minimize superfluous activities under a feasibility constraint, followed by scheduling to minimize makespan) and an integrated single‑stage approach. Experiments on two real‑world and one synthetic dataset show that the integrated method achieves better makespans but struggles with scalability, whereas the decomposed method scales to larger settings.
By Michel Kunkler, Stefanie Rinderle-Ma
The paper evaluates strategies for increasing plan‑execution flexibility by converting sequential plans into partial‑order plans through deordering and reordering. It compares block deordering methods, which restructure causal dependencies, with MaxSAT‑based approaches that optimize within existing causal structures. The study finds that block deordering consistently outperforms MaxSAT in both effectiveness and efficiency, offering anytime solutions and higher flexibility gains per computation time.
By Md. Monjurul Islam, Sabah Binte Noor, Fazlul Hasan Siddiqui, Gahangir Hossain
arXiv:2403. 19883v2 Announce Type: replace Abstract: Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty.
By Frederico Messa, Andr\'e Grahl Pereira