Learning of Lifted Macro-Events for Heuristic-Search Temporal Planning

Learning domain knowledge from small training problems to improve planning performance on arbitrarily sized problems is a highly active research area. Many works explored the use of macro-actions to create "shortcuts" in the search space, at the cost of increasing the branching factor of the problem. In temporal planning, a recent technique proposes to equip a heuristic-search temporal planner with selected "macro-events": a "shortcut" mechanism similar to macro-actions but with state-dependent semantics. In this paper, we generalize macro-events to a lifted representation, making them independent of specific problem objects. We devise a fully automated framework that, given a domain and a collection of small training problems, constructs and selects a suitable set of lifted macro-events. We define a learning pipeline that mixes the optimization of the statistical expectation on an abstraction of the problem with an empirical refinement of the selection on a validation set. We experimentally show that the proposed approach scales to complex problems, yielding substantial improvements over the baseline.