并行贪心最佳优先搜索中生成与评估的解耦（扩展版）
Decoupling Generation and Evaluation for Parallel Greedy Best-First Search(extended version)
AI总结：
针对受限并行贪心最佳优先搜索中线程需空闲等待符合约束的扩展状态、约束执行成本高的问题，提出将状态生成与评估解耦的改进方法，显著提升状态评估速率，优化搜索性能。
摘要：
为理解并控制并行搜索的搜索行为，近期研究提出了一类受限并行贪心最佳优先搜索算法，仅扩展满足特定约束的状态。但执行这类约束的成本较高，线程必须空闲等待，直至符合扩展约束的状态出现。我们提出一种针对受限并行搜索的改进方案，将状态生成与状态评估解耦，显著提升了状态评估速率，进而实现了更优的搜索性能。
英文摘要：
In order to understand and control the search behavior of parallel search, recent work has proposed a class of constrained parallel greedy best-first search algorithms which only expands states that satisfy some constraint.However, enforcing such constraints can be costly, as threads must be waiting idly until a state that satisfies the expansion constraint is available. We propose an improvement to constrained parallel search which decouples state generation and state evaluation and significantly improves state evaluation rate, resulting in better search performance.
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。
