Generalizing over Several Learning Settings

We recapitulate regular one-shot learning from membership and equivalence queries, positive and negative finite data. We present a meta-algorithm that generalizes over as many settings involving one or more of those information sources as possible and covers the whole range of combinations allowing inference with polynomial complexity. The algorithm uses the concept of an observation table as a means to perform and document the inference process at the same time.