AI 中文总结
研究在预算约束下从预设计划提取高效用子计划的问题，提出通过移除低效用目标动作约简计划，证明决策变体NP完全，给出基于超额预订规划和整数线性规划的精确方法，还引入优化ILP公式提升效率。
AI 中文摘要
在一些实际应用中，由于新施加的预算约束，计划可能随后变得不可行，然而同时，仅使用计划的原始动作及其顺序是强制性的。本文研究从预先计算的计划中提取一个有效子计划的问题，该子计划在遵守成本约束的同时最大化效用。每个目标被赋予一个效用值，通过移除支持低效用目标的动作来约简计划，同时保持可执行性和原始动作顺序。我们证明该决策变体是NP完全的，并提出两种精确方法来解决它：一种通过超额预订规划（OSP），另一种通过整数线性规划（ILP）。本文扩展了我们之前在ICAPS 2026上发表的工作。虽然核心框架保持不变，但我们进一步引入了一个优化的ILP公式，显著减小了模型规模并提高了计算效率。
英文摘要
In some real applications a plan may later become unfeasible due to newly imposed budget constraints, yet, at the same time, using only the original actions of the plan and their order is mandatory. In this paper, we study the problem of extracting, from a precomputed plan, a valid subplan that maximizes utility while respecting a cost bound. Each goal is given a utility value and the plan is reduced by removing actions that support low-utility goals, while preserving both executability and the original action order. We show the decision variant is NP-complete and propose two exact methods to solve it: one via oversubscription planning (OSP) and another via Integer Linear Programming (ILP). This paper extends our previous work published at ICAPS 2026 (Del Toro, Fuentetaja, and García-Olaya 2026b). While the core framework remains as introduced there, we further introduce a refined ILP formulation that significantly decreases the model size and improves computational efficiency.
CommentsExtended version of a paper accepted at ICAPS 2026 (doi: 10.1609/icaps.v36i1.42849). Includes a new model and expanded experimental results
