Reflective Inductive Inference of Recursive Functions
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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 example sequences. Here, we arrive at four different types of reflection: reflection in the limit, optimistic, pessimistic and exact reflection.
Then, for learning in the limit, for consistent learning of three different types and for finite learning, we compare the learning power of reflective IIMs with each other as well as with the one of standard IIMs.
Finally, we compare reflective learning from arbitrary input sequences with reflective learning from canonical input sequences. In this context, an open question regarding total-consistent identification could be solved: it holds T-CONSa ⊂ T-CONS .
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