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• W4386031891 abstract "In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment." @default.
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• W4386031891 date "2023-08-18" @default.
• W4386031891 modified "2023-09-29" @default.
• W4386031891 title "Cautious Bayesian Optimization: A Line Tracker Case Study" @default.
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• W4386031891 doi "https://doi.org/10.3390/s23167266" @default.
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