Learning Relational Decision Trees for Guiding Heuristic Planning

The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computation effort. On the contrary, where this is not true, heuristics planners compute loads of useless node evaluations that make them scale-up poorly. In this paper we present a novel approach for boosting the scalability of heuristic planners based on automatically learning domain-specific search control knowledge in the form of relational decision trees. Particularly, we define the learning of planning search control as a standard classification process. Then, we use an off-theshelf relational classifier to build domain-specific relational decision trees that capture the preferred action in the different planning contexts of a planning domain. These contexts are defined by the set of helpful actions extracted from the relaxed planning graph of a given state, the goals remaining to be achieved, and the static pr...

• Type: Conference
• Year: 2008
• Where: AIPS
• Authors: Tomás de la Rosa, Sergio Jiménez, Daniel Borrajo
• Keywords: AIPS 2008, Artificial Intelligence, Decision Trees, Domain-specific Relational Decision, Relational Decision Trees