On the effective configuration of planning domain models

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 paper, we investigate how the performance of planners is affected by domain model configuration. We introduce a fully automated method for this configuration task, and show in an extensive experimental analysis with six planners and seven domains that this process (which can, in principle, be combined with other forms of reformulation and configuration) can have a remarkable impact on performance across planners. Furthermore, studying the obtained domain model configurations can provide useful information to effectively engineer planning domain models.