On the Importance of Domain Model Configuration for Automated Planning
  Engines
  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.
