Learning, induction, deduction

Learning process starts from induction.
For example, I discover a chair. I learn that such a thing is a chair.
But what is "such a thing"? No previous experience.

I suppose I may ask questions about it. Next time I see a chair quite different from the first one I probably don't recognize it as a chair.

But I ask and I get an answer again. Later, I recognize many chairs without any help.

From different chairs, I have built a general idea of what a chair may look like.
Induction
Inductive reasoning is moving from a set of specific facts to a general conclusion. This type of reasoning leads to over-generalization. Not all seats are called "chairs". Learning process tends to remove these errors through more experience.
Deduction
Deductive reasoning reaches a conclusion by following a logical inference from general rules applied to a specific fact.
Back to the first example of a chair. Deductive reasoning may be used in order to validate or invalidate a guess.
"From my point of view, this is a chair." How to be sure? I use general rules.
This must be of a given size and shape allowing human beings to sit on it...

Induction is in process whenever experience is building up new knowledge.
Deduction is working whenever knowledge is being used.
This assertion remains true within science domain. Theories are validated from inductive reasoning. Deductive reasoning applies once theories are validated.

Strong AI shall be self-learning

We may "bootstrap" a strong AI from a kind of knowledge database. After this initial bootstrap, does this AI remain unchanged?
"Real" strong artificial intelligence implies ability of discovering acceptable solutions from past experience when new encountered situations are partially or completely unknown.
How to evaluate acceptability of solutions?
A comparison between expected computed consequences and real events gives an evaluation. The result of this comparison is new knowledge itself.
Human beings constantly increase their knowledge this way. Artificial Intelligence shall use self-learning in order to think in the same way as humans do.
Real artificial intelligence = getting smarter from cumulated experience.
Once implemented such a property, is it compulsory to "bootstrap" a huge amount of knowledge?
Self-learning capability is a key feature for strong AI.