相关性分数：一种类似地标的规划启发式方法
Relevance Score: A Landmark-Like Heuristic for Planning
AI总结：
本文提出一种新颖的“相关性分数”启发式方法，用于识别出现在大多数而非全部计划中的事实或动作，并在缺乏非平凡地标的规划问题上显著提升搜索性能。
摘要：
地标是出现在规划问题所有有效解中的事实或动作。它们已被成功用于计算指导计划搜索的启发式方法。我们通过定义一种新颖的“相关性分数”来扩展这一概念，该分数有助于识别在实现任何给定目标的大多数（但并非全部）计划中出现的事实或动作。我们描述了一种计算此相关性分数的方法，并将其用作计划搜索中的启发式方法。我们使用基准规划问题，将我们方法的性能与最先进的基于地标的启发式规划方法的性能进行了实验比较。虽然原始的基于地标的启发式方法在具有明确地标的问题上表现更好，但我们的方法在缺乏非平凡地标的问题上显著提高了性能。
英文摘要：
Landmarks are facts or actions that appear in all valid solutions of a planning problem. They have been used successfully to calculate heuristics that guide the search for a plan. We investigate an extension to this concept by defining a novel "relevance score" that helps identify facts or actions that appear in most but not all plans to achieve any given goal. We describe an approach to compute this relevance score and use it as a heuristic in the search for a plan. We experimentally compare the performance of our approach with that of a state of the art landmark-based heuristic planning approach using benchmark planning problems. While the original landmark-based heuristic leads to better performance on problems with well-defined landmarks, our approach substantially improves performance on problems that lack non-trivial landmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
