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.

Stability, positive or negative feedback

A simple closed loop controller must be stable. Feedback must be negative at low frequencies and static operation. Stability of a closed loop controller is predictable from its open loop behaviour: gain and phase as a function of the frequency. Open loop gain must decrease with frequency, in order to become less than 1 before phase shift reach a critical point (phase margin and gain margin are both required).
An intelligent being is a non-linear system. The main closed loop of an intelligent being shall remain stable. Perception of self-reaction consequences must not exceed the initial perception. However this situation happens from time to time to intelligent beings:
Pain ==> Quick reaction ==> more pain ==> more reaction.
Loss of stability may lead to loss of life: each new attempt in the same way leads to a worse situation. Control elements of an intelligent being detect that a previous action is inefficient and select another strategy.
For example, attacking may be better than fleeing.

Learning capabilities facilitate new accurate response elaboration.
Control elements of an intelligent being switch from a possible behavior to another one in order to retrieve stability.
Control elements of an intelligent being learn from experience how to do and how to select what to do.
Increasing predictability in a control element increases stability and shortens response time.
Learning capability tends to increase the forecasting horizon of an intelligent being.


Model predictive control (see wikipedia) uses predictive models in order to enhance control elements.