动态世界中的最优规划
Optimal Planning in a Dynamic World
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中文总结 AI 辅助
本文提出任意开始时间规划问题，通过复合到达时间函数（cATF）紧凑编码最优规划，使动态环境中规划可快速查询，实验证明优于重规划方法。
中文摘要 AI 辅助
背景：我们解决了当可行状态或动作集合随时间变化时的规划问题。例如，在移动障碍物间的路径规划问题（有时称为SIPP）中，处于特定位置的可行性会随着障碍物的移动而改变。或者，登上特定列车的动作仅在列车停靠在车站时可行。这种动态性意味着最优规划及其持续时间会因执行开始时间的不同而改变。在实践中，执行开始时间通常在规划完成或另一个代理给出许可之前是未知的。然而，大多数先前的规划工作要么忽略动态性，要么假设已知的开始时间。这使得评估状态和动作的可行性变得简单，但在某些应用中并不实用。目标：在本文中，我们放宽了已知开始时间的假设。我们定义了“任意开始时间规划”的设置，并为其提供了算法。方法：我们提出了一种称为复合到达时间函数（cATF）的数据结构，它紧凑地将最优规划编码为开始时间的函数。我们提供了基于启发式图搜索的通用规划算法，这些算法通过沿边传播函数而不是标量成本来组装cATF。结果：我们证明了cATF的大小至多与问题规模成线性关系。针对SIPP特定问题的实现的实验评估表明，在困难问题上，依赖重新规划的代理经常失败，而使用cATF的任意开始时间算法在知道执行开始时间后可以快速查找最优规划。结论：通过为时间相关规划提供高效的表示和推理，这项工作为动态世界中的规划奠定了基础。
英文摘要
Background: We address the problem of planning when the set of feasible states or actions changes over time. For example, in the problem of path planning among moving obstacles (sometimes known as SIPP), the feasibility of being at a particular location can change as the obstacles move. Or, the action of boarding a particular train is feasible only while it is stopped at the station. This dynamism means that the optimal plan and its duration can change depending on when execution begins. In practice, execution start time is often unknown until planning has completed or another agent gives the go-ahead. However, most prior planning work either ignores dynamism or assumes a known start time. This makes it straightforward to assess state and action feasibility but is impractical for some applications. Objectives: In this paper, we relax the assumption of a known start time. We define the setting of {\em any-start-time planning} and provide algorithms for it. Methods: We present a data structure called a compound arrival time function (cATF) that compactly encodes the optimal plan as a function of start time. We provide general-purpose planning algorithms, based on heuristic graph search, that assemble cATFs by propagating functions along edges instead of scalar costs. Results: We prove that the size of a cATF is at most linear in the problem size. An experimental evaluation of an implementation for the specific problem of SIPP shows that, on difficult problems, agents that rely on replanning often fail, while any-start-time algorithms using cATFs can quickly look up the optimal plan once the execution start time is known. Conclusions: By enabling efficient representations and reasoning for time-dependent plans, this work provides a foundation for planning in dynamic worlds.
发表机构
- University of New Hampshire（新罕布什尔大学）
- Ben-Gurion University of the Negev（内盖夫本-古里安大学）
机构由 AI 辅助整理，请以论文原文为准。
