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A robust-satisficing approach based on info-gap theory is suggested as a solution for a spatial search-planning problem with imprecise probabilistic data. A group of agents are searching predefined patches of land for stationary targets, given an a priori probability map of the targets' locations. This prior probabilistic information is assumed to be severely uncertain and may contain large errors. An analysis of a simplified case shows that in some situations, one might prefer a different strategy than the expected-utility maximizing (EUM) one in terms of robustness to uncertainty. Deterministic numeric results confirm the theoretical predictions for more complex cases. Finally, stochastic numeric analysis of robust-satisficing solutions on a large group of much more complex, randomly generated cases, reveals an interesting behavior of a consolidation of effort in specific cells and implies the potential of robust satisficing in more realistic scenarios. As the robustness to uncertainty comes at the expense of the expected utility, one must choose its decisions carefully. However, it is shown that in various circumstances, one obtains results that are superior to the EUM strategy in terms of robustness, while sacrificing almost no expected utility.