Heuristic search guides the exploration of states via heuristic functions estimating remaining cost. Symbolic search instead replaces the exploration of individual states with that of state sets, compactly represented using binary decision diagrams (BDDs). In cost-optimal planning, heuristic explicit search performs best overall, but symbolic search performs best in many individual domains, so both approaches together constitute the state of the art. Yet combinations of the two have so far not been an unqualified success, because (i) must be applicable to sets of states rather than individual ones, and (ii) the different state partitioning induced by may be detrimental for BDD size. Many competitive heuristic functions in planning do not qualify for (i), and it has been shown that even extremely informed heuristics can deteriorate search performance due to (ii).

中文翻译：

---

促进最佳符号规划：操作员潜力启发法


启发式搜索通过估计剩余成本的启发式函数来指导对状态的探索。相反，符号搜索将单个状态的探索替换为状态集的探索，并使用二元决策图 (BDD) 进行紧凑表示。在成本最优规划中，启发式显式搜索总体表现最佳，但符号搜索在许多单独领域表现最佳，因此这两种方法共同构成了现有技术。然而，到目前为止，两者的组合还没有取得绝对的成功，因为 (i) 必须适用于状态集而不是单个状态，并且 (ii) 引起的不同状态划分可能对 BDD 大小有害。规划中的许多竞争性启发式函数不符合 (i)，并且事实证明，即使是极其明智的启发式函数也会因 (ii) 而降低搜索性能。