发表机构
Monash University（莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对 A-K-MDP 算法可能跳过更好抽象状态的问题，提出窗口化 A-K-MDP，在除数窗口内生成并评估所有可行分区，在 33 个实例中改进 25 个、持平 8 个。
AI 中文摘要
马尔可夫决策过程（MDPs）被用于支持生物多样性保护中的决策制定，但即使是在小状态空间上，其策略对于保护管理者而言也可能难以解释。K-MDP 方法通过构建最多包含 K 个抽象状态的更简单 MDP 来解决这一问题。我们表明，先前提出的 A-K-MDP 算法依赖于使用二分搜索选择离散化除数，可能会跳过更好的抽象状态。为解决此问题，我们提出了窗口化 A-K-MDP，一种在声明的除数窗口内生成每个不同可行分区并评估候选方案，直到达到理想价值损失（J = 0）或耗尽候选家族的算法。在 33 个 K-MDP 实例中，窗口化方法改进了 25 个实例，持平 8 个实例。
英文摘要
Markov decision processes (MDPs) are used to support decision-making in conservation of biodiversity, but policies, even over small state spaces, can be difficult to interpret for conservation managers. K-MDP methods address this problem by building simpler MDPs with at most K abstract states. We show that the previously proposed A-K-MDP algorithm that relies on selecting a discretisation divisor using binary search can skip better abstract states. To fix this issue, we propose Windowed A-K-MDP, an algorithm that generates every distinct feasible partition induced within a declared divisor window and evaluates candidates until reaching the ideal value loss (J = 0) or exhausting the family of candidates. Across 33 K-MDP instances, Windowed improved 25 and tied 8.
