Utilizing Landmarks in Euclidean Heuristics for Optimal Planning

An important problem in AI is to construct high-quality heuristics for optimal search. Recently, the Euclidean heuristic (EH) has been proposed, which embeds a state space graph into a Euclidean space and uses Euclidean distances as approximations for the graph distances. The embedding process leverages recent research re-sults from manifold learning, a subfield in machine learning, and guarantees that the heuristic is provably admissible and consistent. EH has shown good perfor-mance and memory efficiency in comparison to other existing heuristics. Our recent works have further im-proved the scalability and quality of EH. In this short paper, we present our latest progress on applying EH to problems in planning formalisms, which provide riche