Recursive Best-First Search (RBFS) is a heuristic search algorithm known for its efficient memory usage compared to traditional best-first search methods like A*. Despite its theoretical advantages, RBFS is complex and difficult to teach and to implement, limiting its widespread adoption. To address these challenges, Iterative Linear Best-First Search (ILBFS) was introduced as a simpler, more intuitive alternative while maintaining the linear space complexity of RBFS. In this paper, we present the first implementation of ILBFS, validate its memory usage and node expansion order claims, and explore critical aspects of its implementation, such as tie-breaking and node deletion mechanisms. Our findings demonstrate that ILBFS can serve as an effective stepping stone for researchers and practitioners looking to use memory efficient best-first search methods, facilitating the adoption of RBFS-like algorithms.

翻译：递归最佳优先搜索（RBFS）是一种启发式搜索算法，以其相较于A*等传统最佳优先搜索方法更高效的内存使用而闻名。尽管RBFS具有理论优势，但其算法复杂，教学与实现难度较高，限制了其广泛应用。为解决这些问题，迭代线性最佳优先搜索（ILBFS）被提出作为一种更简单、更直观的替代方案，同时保持了RBFS的线性空间复杂度。本文首次实现了ILBFS，验证了其内存使用和节点扩展顺序的相关论断，并探讨了其实现中的关键方面，如平局决胜机制和节点删除机制。我们的研究结果表明，对于寻求使用内存高效最佳优先搜索方法的研究者和实践者而言，ILBFS可以作为一个有效的入门途径，有助于推动类RBFS算法的采用。