Accurate action models are essential for efficiently solving automated planning tasks. An accurate action model allow the planner to precisely foresee the consequences of executing actions in a given environment and therefore to find robust and good quality plans. But when addressing planning tasks in the real world, even hand-coding a simple STRIPS action model is complex, thus defining action models capturing further features, like the execution duration or costs, becomes more difficult. Moreover, if these features can be captured at a given instant they may vary over time. In this paper we automatically model the duration of action execution as relational regression trees learned from observing plan executions. And we show how planners find better plans after incorporating these models to their domain definition
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