In recent years, there has been renewed interest in closing the performance gap between state-of-the-art planning solvers and generalized planning (GP), a research area of AI that studies the automated synthesis of algorithmic-like solutions capable of solving multiple classical planning instances. One of the current advancements has been the introduction of Best-First Generalized Planning (BFGP), a GP algorithm based on a novel solution space that can be explored with heuristic search, one of the foundations of modern planners. This paper evaluates the application of parallel search techniques to BFGP, another critical component in closing the performance gap. We first discuss why BFGP is well suited for parallelization and some of its differentiating characteristics from classical planners. Then, we propose two simple shared-memory parallel strategies with good scaling with the number of cores.

翻译：近年来，学界重新关注于缩小最先进规划求解器与广义规划之间的性能差距。广义规划是人工智能的一个研究领域，致力于研究能够解决多个经典规划实例的类算法解决方案的自动合成。当前进展之一是引入了最佳优先广义规划，这是一种基于新型解空间的广义规划算法，该解空间可通过启发式搜索进行探索，而启发式搜索是现代规划器的基础之一。本文评估了并行搜索技术在最佳优先广义规划中的应用，这是缩小性能差距的另一个关键组成部分。我们首先讨论了为何最佳优先广义规划适合并行化，及其与经典规划器的一些区别性特征。随后，我们提出了两种简单的共享内存并行策略，这些策略在核心数量增加时具有良好的扩展性。