A General Approach for Configuring PDDL Problem Models

The development of a large number of domain-independent planners is leading to the use of planning engines in a wide range of applications. This is despite the complexity issues inherent in plan generation, which are exacerbated by the separation of planner logic from domain knowledge. However, this separation supports the use of reformulation and configuration techniques, which transform the model representation in order to improve the planner’s performance. In this paper, we investigate how the performance of domainindependent planners can be improved by problem model configuration. We introduce a fully automated method for this configuration task, that considers problem-specific aspects extracted by exploiting a problemand domain-independent representation of the instance. Our extensive experimental analysis shows that this reformulation technique can have a significant impact on planners’ performance.

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