Planning as theorem proving in situation calculus was abandoned 50 years ago as an impossible project. But we have developed a Theorem Proving Lifted Heuristic (TPLH) planner that searches for a plan in a tree of situations using the A* search algorithm. It is controlled by a delete relaxation-based domain independent heuristic. We compare TPLH with Fast Downward (FD) and Best First Width Search (BFWS) planners over several standard benchmarks. Since our implementation of the heuristic function is not optimized, TPLH is slower than FD and BFWS. But it computes shorter plans, and it explores fewer states. We discuss previous research on planning within KR\&R and identify related directions. Thus, we show that deductive lifted heuristic planning in situation calculus is actually doable.
翻译：摘要：50年前，在情景演算中将规划作为定理证明被放弃了，因为这是不可能的项目。但我们已经发展了一个基础启发式的定理证明（TPLH）规划器，使用A *搜索算法在情况树中搜索计划。它被一个删除弛缓态的领域无关启发控制。我们在几个标准基准测试中将TPLH与Fast Downward（FD）和Best-First宽度搜索（BFWS）规划器进行比较。由于我们对启发函数的实现没有进行优化，TPLH比FD和BFWS慢。但它计算出更短的计划，并探索了更少的状态。我们讨论了以前关于KR＆R中规划的研究，并确定了相关方向。因此，我们表明基于演绎的基础启发式规划实际上是可行的。
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