was able to solve at most 23 instances using the tested encodings. Moreover, in both experiments [<span style="font-variant:small-caps;">clasp</span>]{}failed to find a model for most of the double as well as for some double-variant and triple instances. Also [<span style="font-variant:small-caps;">wasp</span>]{}with [<span style="font-variant:small-caps;">Quick[PUP]{}</span>]{}solved only 12 and 15 instances using [PUP]{}-E1 and [PUP]{}-E2 respectively. The main reason for this discrepancy between our results and the ones obtained by [@DBLP:conf/iaai/TeppanFF12] is due to inability of the underlying ASP solver to generate new units on-the-fly. Therefore, the number of ground literals denoting available units is determined in the grounding step and cannot be changed later. [@DBLP:conf/iaai/TeppanFF12], instead, try to quickly build a solution by using new units whenever possible. This strategy appears to be successful for greedy algorithms, but works poorly as a heuristic for [<span style="font-variant:small-caps;">wasp</span>]{}.

The two remaining heuristics assign zones and sensors to already existing units first. This allowed [<span style="font-variant:small-caps;">wasp</span>]{}to outperform [<span style="font-variant:small-caps;">clasp</span>]{}and [<span style="font-variant:small-caps;">Quick[PUP]{}</span>]{}in all experiments. The best result was observed for [<span style="font-variant:small-caps;">wasp</span>]{}with [<span style="font-variant:small-caps;">Pred</span>]{}, which solved *all* instances independently of the encoding. Moreover, [PUP]{}-E2 allowed [<span style="font-variant:small-caps;">wasp</span>]{}to reduce its average solving time to only 27 sec. Finally, we would like to emphasize the fact that [<span style="font-variant:small-caps;">Pred</span>]{}allowed [<span 