Alfonso Gerevini, Alessandro Saetti, and Ivan Serina
LPGis a planner that performed very well in the last International planning competition (2002). The system is based on a stochastic local search procedure, and it incorporates several heuristic features. In this paper we experimentally analyze the most important of them with the goal of understanding and evaluating their impact on the performance of the planner. In particular, we examine three heuristic functions for evaluating the search neighborhood and some settings of the "noise" parameter, that randomizes the next search step for escaping from local minima. Moreover, we present and analyze additional heuristic techniques for restricting the search neighborhood and for selecting the next inconsistency to handle. The experimental results show that the use of such techniques significantly improves the performance of the planner.