RIFO, as has been proposed by Nebel et al., is a method that can automatically detect irrelevant information in planning tasks. The idea is to remove such irrelevant information as a pre-process to planning. While RIFO has been shown to be useful in a number of domains, its main disadvantage is that it is not completeness preserving. Furthermore, the pre-process often takes more running time than nowadays state-of-the-art planners, like FF, need for solving the entire planning task. We introduce the notion of relaxed irrelevance, concerning actions which are never needed within the relaxation that heuristic planners like FF and HSP use for computing their heuristic values. The idea is to speed up the heuristic functions by reducing the action sets considered within the relaxation. Starting from a sufficient condition for relaxed irrelevance, we introduce two preprocessing methods for filtering action sets. The first preprocessing method is proven to be completeness-preserving, and is empirically shown to terminate fast on most of our testing examples. The second method is fast on all our testing examples, and is empirically safe. Both methods have drastic pruning impacts in some domains, speeding up FF's heuristic function, and in effect the planning process.

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple and applies to domains with both categorical and continuous variables. The experiments demonstrate that REMBO can effectively solve high-dimensional problems, including automatic parameter configuration of a popular mixed integer linear programming solver.