 Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
<clarity>  Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully and partially labeled settingsthat are asymptotically optimal , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.
 Despite their NP-hardness, we are able to again develop efficient algorithms for 2D fully- and partially-labeled settings , in expectation, assuming that the initial configuration is randomly selected . Simulation studies confirm the effectiveness of our algorithms in comparison to natural greedy best-first algorithms.