The development of domain-independent planners within the AI planning community is leading to “off-the-shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach—in which planners and domain knowledge are modules of larger software applications—that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this article, we investigate how the performance of domain-independent planners is affected by domain model configuration, i.e. the order in which elements are ordered in the model, particularly in the light of planner comparisons. We then introduce techniques for the online and offline configuration of domain models, and we analyse the impact of domain model configuration on other reformulation approaches, such as macros.

人工智能规划社区内独立于领域的规划器的发展正在导致可用于广泛应用的“现成”技术。此外，它允许采用模块化方法——其中规划者和领域知识是较大软件应用程序的模块——便于替换或改进单个模块，而无需更改系统的其余部分。这种方法还支持使用重构和配置技术，这些技术可以改变模型的表示方式，以提高计划生成的效率。在本文中，我们研究领域独立规划器的性能如何受到领域模型配置的影响，即模型中元素的排序顺序，特别是在规划器比较的情况下。