Abstract
Current study results on Robotics have been published. According to news originating from Boulder, Colorado, by NewsRx correspondents, research stated, "We introduce a new variant of contextual mult i-armed bandits (CMABs) called observation-augmented CMABs (OA-CMABs) wherein a robot uses extra outcome observations from an external information source, e.g. humans. In OA-CMABs, external observations are a function of context features an d thus provide evidence on top of observed option outcomes to infer hidden param eters." Our news journalists obtained a quote from the research from the University of C olorado, "However, if external data is error-prone, measures must be taken to pr eserve the correctness of inference. To this end, we derive a robust Bayesian in ference process for OA-CMABs based on recently developed probabilistic semantic data association techniques, which handle complex mixture model parameter priors and hybrid discrete-continuous observation likelihoods for semantic external da ta sources. To cope with combined uncertainties in OA-CMABs, we also derive a ne w active inference algorithm for optimal option selection based on approximate e xpected free energy minimization. This generalizes prior work on CMAB active inf erence by accounting for faulty observations and non-Gaussian distributions."
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