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Current evaluation functions for heuristic planning are expensive to compute.In numerous planning problems these functions provide good guidance to thesolution, so they are worth the expense. However, when evaluation functions aremisguiding or when planning problems are large enough, lots of node evaluationsmust be computed, which severely limits the scalability of heuristic planners.In this paper, we present a novel solution for reducing node evaluations inheuristic planning based on machine learning. Particularly, we define the taskof learning search control for heuristic planning as a relationalclassification task, and we use an off-the-shelf relational classification toolto address this learning task. Our relational classification task captures thepreferred action to select in the different planning contexts of a specificplanning domain. These planning contexts are defined by the set of helpfulactions of the current state, the goals remaining to be achieved, and thestatic predicates of the planning task. This paper shows two methods forguiding the search of a heuristic planner with the learned classifiers. Thefirst one consists of using the resulting classifier as an action policy. Thesecond one consists of applying the classifier to generate lookahead stateswithin a Best First Search algorithm. Experiments over a variety of domainsreveal that our heuristic planner using the learned classifiers solves largerproblems than state-of-the-art planners.

Scaling up Heuristic Planning with Relational Decision Trees