Abstract

The planning as heuristic search framework, initiated by the planners ASP from Bonet, Loerincs and Geffner, and HSP from Bonet and Geffner, lead to some of the most performant planners, as demonstrated in the two previous editions of the International Planning Competition. We focus in this paper on a technique introduced by Hoffmann and Nebel in the FF planning system for calculating the heuristic, based on the extraction of a solution from a planning graph computed for the relaxed problem obtained by ignoring deletes of actions. This heuristic is used in a forward-chaining search algorithm to evaluate each encountered state. As a side effect of the computation of this heuristic, more information is derived from the planning graph and its solution, namely the helpful actions which permit FF to concentrate its efforts on more promising ways, forgetting the other actions in a local search algorithm. We introduce a novel way for extracting information from the computation of the heuristic and for tackling with helpful actions, by considering the high quality of the plans computed by the heuristic function in numerous domains. For each evaluated state, we employ actions from these plans in order to find the beginning of a valid plan that can lead to a reachable state, that will often bring us closer to a solution state. The lookahead state thus calculated is then added to the list of nodes that can be chosen to be developed following the numerical value of the heuristic. We use this lookahead strategy in a complete best-first search algorithm, modified in order to take into account helpful actions by preferring nodes that can be developed with such actions over nodes that can be developed with actions that are not considered as helpful. We then provide an empirical evaluation which demonstrates that in numerous planning benchmark domains, the performance of heuristic search planning and the size of the problems that can be handled have been drastically improved, while in more “difficult” domains these strategies remain interesting even if they sometimes degrade plan quality.

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