There is not a clear reason why "reasoning" and "learning" must be unified. Can you elaborate on the advantages of such an approach?
To answer that question I would have to know how you are defining those terms.
The "learning" problem in AGI is difficult partly because GOFAI knowledge representation schemes are usually very cumbersome (with frames, microtheories, modal operators for temporal / epistemological aspects, etc). My logic is very minimalistic, almost structureless. This makes learning easier since learning is a search for hypotheses in the hypothesis space.
With a minimalist logic, the hypothesis space will be large, posing a huge search problem.
The point of all those "cumbersome" additions to basic logic is essentially to allow learning and reasoning algorithms to narrow down the search space in contextually appropriate ways.
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