将图规划为（动态）CSP：在图规划中利用EBL、DDB和其他CSP搜索技术
Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan
摘要：
本文回顾了Graphplan的规划图和动态约束满足问题之间的联系，并激发了需要适应CSP搜索技术的Graphplan算法。然后，它描述了如何基于解释的学习，依赖导向回溯，动态变量排序，前向检查，粘性值和随机重新启动搜索策略可以适应Graphplan。实证结果表明，这些增强显着提高Graphplan的性能（高达1000倍的加速）的几个基准问题。特别注意的是基于解释的学习和依赖导向的回溯技术，因为它们是经验发现是最有用的，在提高性能的Graphplan。
英文摘要：
This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
