Anytime search algorithms are useful for planning problems where a solution is desired under a limited time budget. Anytime algorithms first aim to provide a feasible solution quickly and then attempt to improve it until the time budget expires. On the other hand, parallel search algorithms utilize the multithreading capability of modern processors to speed up the search. One such algorithm, ePA*SE (Edge-Based Parallel A* for Slow Evaluations), parallelizes edge evaluations to achieve faster planning and is especially useful in domains with expensive-to-compute edges. In this work, we propose an extension that brings the anytime property to ePA*SE, resulting in A-ePA*SE. We evaluate A-ePA*SE experimentally and show that it is significantly more efficient than other anytime search methods. The open-source code for A-ePA*SE, along with the baselines, is available here: https://github.com/shohinm/parallel_search

翻译：即时搜索算法在需要在有限时间预算内获得解决方案的规划问题中非常有用。这类算法首先旨在快速提供可行方案，随后在时间预算耗尽前尝试改进该方案。另一方面，并行搜索算法利用现代处理器的多线程能力来加速搜索过程。其中，ePA*SE（面向慢速评估的边缘并行A*算法）通过并行化边缘评估实现更快速的规划，尤其适用于边缘计算成本高昂的领域。本研究提出一种扩展方法，将即时属性引入ePA*SE，从而得到A-ePA*SE算法。我们通过实验评估了A-ePA*SE，结果表明其效率显著优于其他即时搜索方法。A-ePA*SE及其基线的开源代码可在此获取：https://github.com/shohinm/parallel_search