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:在情境演算中作为定理证明的规划在50年前被放弃了,因为它是一个不可能的项目。但是我们开发了一个带启发式定理证明规划器(Theorem Proving Lifted Heuristic (TPLH) Planner),它使用A*搜索算法在情境树中搜索计划。它由一个基于删除松弛的领域独立启发式控制。我们将TPLH与Fast Downward (FD)和Best First Width Search (BFWS)规划器在多个标准基准测试中进行了比较。由于我们的启发式函数实现尚未优化,TPLH比FD和BFWS慢。但它计算出更短的计划,并探索更少的状态。我们讨论了先前在KR&R中的规划研究,并确定了相关的研究方向。因此,我们证明了用情境演算中的演绎式带启发式规划是可行的。