提出改进的双向启发式搜索算法，可控制解的次优度。
Bidirectional Bounded-Suboptimal Heuristic Search with Consistent Heuristics
- 基于BAE*设计适用于次优搜索的变体
- 实验显示不同算法在不同场景表现更优
- 适合需要权衡速度与解质量的研究者
近年来，双向启发式搜索取得了重要理论进展并催生了新算法。尽管多数研究聚焦于最优搜索方法，本文关注有界次优的双向搜索，即指定解代价的次优性上限。我们基于最优双向搜索算法BAE*（针对一致启发式）提出若干专为次优情境设计的变体。通过实验评估，将这些新算法与其他有界次优双向算法及标准加权A*进行比较。结果表明，各算法在不同条件下各有优势，凸显了各类方法的优劣特点。
原文摘要 · Abstract (English)
Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal search methods, this paper focuses on bounded-suboptimal bidirectional search, where a bound on the suboptimality of the solution cost is specified. We build upon the state-of-the-art optimal bidirectional search algorithm, BAE*, designed for consistent heuristics, and introduce several variants of BAE* specifically tailored for the bounded-suboptimal context. Through experimental evaluation, we compare the performance of these new variants against other bounded-suboptimal bidirectional algorithms as well as the standard weighted A* algorithm. Our results demonstrate that each algorithm excels under distinct conditions, highlighting the strengths and weaknesses of each approach.
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