Info-Gap Approach to Multi Agent Search Under Severe Uncertainty
Itay Sisso, Tal Y. Shima, Yakov Ben‐Haim · 2009
A robust satisflcing approach based on info-gap theory is suggested as a solution for a spatial search planning problem with imprecise probabilistic data. In the presented problem, a group of agents (uninhabited aerial vehicles (UAVs)) are searching predeflned patches of land for stationary ground 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 simplifled case shows that in some situations one might prefer a difierent strategy than the expected utility maximizing one, in terms of robustness to uncertainty. Deterministic numeric results conflrm the theoretical predictions for more complex cases. Finally, stochastic numeric analysis of robust satisflcing solutions on a group of 50, much more complex, randomly generated cases, reveals an interesting behavior of a consolidation of efiort in speciflc cells, and implies the potential of robust satisflcing 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 throughout the work that, in various circumstances, one obtains results which are signiflcantly superior to the expected utility maximizing strategy in terms of robustness, while sacriflcing almost no expected utility.