提出新搜索算法，让连续控制参数成为决策点。
Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
- 用延迟部分展开策略处理无限参数空间的搜索
- 在特定条件下证明了算法渐进完备性
- 适合需精细调控的自动化规划任务
在自动化规划中，控制参数通过引入连续数值决策变量扩展了标准动作表示。现有最先进方法将控制参数作为嵌入约束与其他时间与数值限制一同处理，因而隐式地将其视为额外约束而非搜索空间中的真正决策点。本文提出一种高效替代方案，明确将控制参数作为系统搜索框架内的真实决策点。我们开发了一种基于启发式的最佳优先搜索算法，可在由控制参数定义的无限决策空间中运行，并在某些条件下证明了其极限完备性。该算法利用延迟部分展开思想，即状态不完全展开，而是增量式扩展其部分后继。实验结果表明，该新型搜索算法在求解涉及控制参数的规划问题时，是现有方法的有力竞争者。
原文摘要 · Abstract (English)
In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approaches have primarily handled control parameters as embedded constraints alongside other temporal and numeric restrictions, and thus have implicitly treated them as additional constraints rather than as decision points in the search space. In this paper, we propose an efficient alternative that explicitly handles control parameters as true decision points within a systematic search scheme. We develop a best-first, heuristic search algorithm that operates over infinite decision spaces defined by control parameters and prove a notion of completeness in the limit under certain conditions. Our algorithm leverages the concept of delayed partial expansion, where a state is not fully expanded but instead incrementally expands a subset of its successors. Our results demonstrate that this novel search algorithm is a competitive alternative to existing approaches for solving planning problems involving control parameters.
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