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In this paper, we investigate reflective inductive inference of recursive functions. A reflective IIM is a learning machine that is additionally able to assess its own competence. First, we formalize reflective learning from arbitrary, and from canonical, example sequences. Here, we arrive at four different types of reflection: reflection in the limit, optimistic, pessimistic and exact reflection. Then, we compare the learning power of reflective IIMs with each other as well as with the one ofdoi:10.1016/j.tcs.2008.02.022 fatcat:dbu64ujtxbgmlfaiwjzqlrxfku