Ensemble-Roller: Planning with Ensemble of Relational Decision Trees
نویسندگان
چکیده
In this paper we describe the ENSEMBLE-ROLLER planner submitted to the Learning Track of the International Planning Competition (IPC). The planner uses ensembles of relational classifiers to generate robust planning policies. As in other applications of machine learning, the idea of the ensembles of classifiers consists of providing accuracy for particular scenarios and diversity to cover a wide range of situations. In particular, ENSEMBLE-ROLLER is a bagging approach to learn ensembles of relational decision trees. The control knowledge from different sets of trees is aggregated as a single prediction which is used to sort candidates in a depth-first search.
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