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：摘要：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。本文通过多种与提高启发式信息性正交的强效技术，提升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，并探索了多队列搜索与组合方法在启发式结合中的应用。