🤖 AI Summary
This study addresses the problem of extracting an optimal subplan from an existing plan under a budget constraint, while preserving the original actions and their execution order. The goal is to identify a subplan that respects a given cost upper bound, remains executable, and maximizes utility. The decision variant of this problem is proven to be NP-complete. To tackle it, the authors propose a refined integer linear programming (ILP) formulation that significantly reduces model size and enhances computational efficiency without sacrificing solution accuracy. Together with over-subscription planning (OSP), this ILP approach constitutes one of two exact solution methods. Compared to prior work, the proposed ILP method demonstrates marked improvements in both scalability and empirical performance.
📝 Abstract
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.