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%2C a framework leveraging multi-objective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover%2C we establish a sub-linear bound for cumulative regret%2C supporting BOFD's robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods%2C achieving efficient fleet designs with minimal fleet evaluations.?quality=80&w=800)
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