学习排序传播器优先级，提升约束规划求解器性能
Learning to Rank Propagators in Constraint Programming
用自注意力模型替掉 Choco 里手工写的传播器优先级，698 个难例上多解出 20 个，中位求解时间降了 27.93%。做约束规划或组合优化的人可以看看这套从运行时间学偏好的训练思路。
一项新研究将约束规划求解器中传播器执行顺序的确定问题转化为学习排序任务，替代 Choco 求解器原有的固定手工启发式。
研究使用基于自注意力的模型对实例中的传播器联合打分，训练数据来自随机优先级分配下的求解器运行时间，通过成对偏好和 Plackett-Luce 排序目标学习，并在搜索开始前将预测排序映射到 Choco 的离散优先级。在 XCSP3 实例上评测，按问题类挑选的 698 个最困难测试实例中，学习到的优先级将已解实例数从 634 提升到 654，中位求解时间降低 27.
93%，PAR1 聚合惩罚时间降低 12.65%。
改进幅度因问题类而异，研究还发现在学习优先级与默认优先级之间做简单选择可进一步提升解题数。
Learning to Rank Propagators in Constraint Programming
The order in which propagators are executed can substantially affect the performance of constraint programming solvers, yet propagator priorities are typically determined by fixed, manually designed heuristics. In this work, we explore learning instance-dependent propagator priorities from static problem features. We formulate the task as a learning-to-rank problem, addressing the challenge that the quality of a priority assignment can be measured through solver runtime, while the desired priorities of individual propagators are not directly observable. A self-attention-based model jointly scores the propagators present in an instance and is trained using pairwise preferences derived from solver runtimes measured under randomized propagator priority assignments, with a Plackett-Luce-based ranking objective. Predicted rankings are mapped to the discrete priority levels of the Choco solver before search begins, without modifying its propagation or search procedures. We evaluate the approach on XCSP3 instances and demonstrate that propagator ordering can have a substantial impact on solving time. On a test set of 698 instances, picked out as most difficult by problem class, the learned priorities increase the number of solved instances from 634 to 654, reduce median solver runtime by 27.93%, and reduce aggregate penalized runtime (PAR1) by 12.65% compared with Choco's default priorities. We further observe that improvements vary across problem classes and show that a simple policy selecting between learned and default priorities further increases the number of solved instances. These results demonstrate the potential of learning instance-dependent propagator priorities to improve constraint programming performance, particularly on difficult instances.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Choco、约束规划、学习排序、XCSP3，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Matija Lojović, Mladen Nikolić），经 AITOP 多维评分管线结构化提炼。