Beating LM-cut with LM-cut: Quick Cutting and Practical Tie Breaking for the Precondition Choice Function

Anonymous

Abstract: LM-cut is one of the most popular heuristics in optimal planning that computes strong admissible estimates of the perfect delete relaxation heuristic $h^+$. The heuristic iteratively computes disjunctive action landmarks for the current state, reducing their action costs until no more landmarks with remaining action costs can be found. These landmarks are generated by finding cuts in the justification graph, which depends on a precondition choice function mapping each action to its most expensive precondition according to $h^{max}$. This precondition is not necessarily unique, yet the performance of the heuristic heavily depends on this choice. We introduce and analyze several new tie breaking strategies for the precondition choice function, and evaluate their effectiveness on the IPC benchmarks. Furthermore, we suggest a modification to the computation of the cut, which trades a negligible loss in heuristic accuracy for a significant speedup of the LM-cut computation.