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Learning What to Want: Context-Sensitive Preference Learning
2015
PLoS ONE
We have developed a method for learning relative preferences from histories of choices made, without requiring an intermediate utility computation. Our method infers preferences that are rational in a psychological sense, where agent choices result from Bayesian inference of what to do from observable inputs. We further characterize conditions on choice histories wherein it is appropriate for modelers to describe relative preferences using ordinal utilities, and illustrate the importance of the
doi:10.1371/journal.pone.0141129
pmid:26496645
pmcid:PMC4619741
fatcat:upaicivozzeprgldc2u6huoavi