Current classical planners are very successful in finding (non-optimal) plans, even for large planning instances. To do so, most planners rely on a preprocessing stage that computes a grounded representation of the task. Whenever the grounded task is too big to be generated (i.e., whenever this preprocess fails) the instance cannot even be tackled by the actual planner. To address this issue, we introduce a partial grounding approach that grounds only a projection of the task, when complete grounding is not feasible. We propose a guiding mechanism that, for a given domain, identifies the parts of a task that are relevant to find a plan by using off-the-shelf machine learning methods. Our empirical evaluation attests that the approach is capable of solving planning instances that are too big to be fully grounded.

As modeling details can have a large impact on planner perormance, domain transformation has been a traditional subject of interest in the planning community not only betweenlanguages, but also within languages. Herein, we automatean intra-language transformation method that has as yet beenapplied only manually, and that has never been formally described: action schema splitting, which transforms an actionschema with a big interface (many parameters) into severalschemas with smaller interfaces, exponentially reducing thenumber of ground actions. We spell out this method, characterizing exactly the choice of splits preserving equivalence tothe original schema. Making that choice involves a trade-off between interface size and plan length, which we explore bydesigning automatic domain optimization methods. Our experiments show that these methods can substantially improveperformance on domains with large interfaces.