Computing Planning Centroids and Minimum Covering States Using Symbolic Bidirectional Search

In some scenarios, planning agents might be interested in reaching states that keep certain relationships with respect to a set of goals. Recently, two of these types of states were proposed: centroids, which minimize the average distance to the goals; and minimum covering states, which minimize the maximum distance to the goals. Previous approaches compute these states by searching forward either in the original or a reformulated task. In this paper, we propose several algorithms that use symbolic bidirectional search to efficiently compute centroids and minimum covering states. Experimental results in existing and novel benchmarks show that our algorithms scale much better than previous approaches, establishing a new state-of-the-art technique for this problem.