Bayesian inference is used to model uncertainty in search-and-rescue operations by predicting the likely locations of missing individuals. More specifically, Bayesian inference allows each new piece of information, each observation, to update our beliefs about a situation, refining the decision-making process. For this reason, Bayesian theory is considered highly effective in search-and-rescue missions, helping to complete operations more efficiently. In this context, a study was designed and conducted in a simulated environment to analyze unmanned aerial vehicle (UAV) operators’ responses in a hypothetical search-and-rescue scenario. The study aimed to determine the most effective way to present Bayesian theory results to UAV operators, enabling them to maximize its benefits.
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