“Scaling up Heuristic Planning with Relational Decision Trees” by T. De la Rosa, S. Jimenez, R. Fuentetaja and D. Borrajo “Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are worth the expense. However, when evaluation functions are misguiding or when planning problems are large enough, lots of node evaluations must be computed, which severely limits the scalability of heuristic planners. In this paper, we present a novel solution for reducing node evaluations in heuristic planning based on machine learning…”