Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics.

翻译：启发式搜索是解决规划问题的强大方法，数值规划也不例外。本文通过多种与提升启发式信息性正交的强大技术，增强了数值规划中启发式搜索的性能：数值新颖性启发式、曼哈顿距离启发式，以及探索使用多队列搜索和组合策略来结合多种启发式。