Recall

Knowledge can be represented as strings and trees of nodes connected together. In order to retrieve information and obtain a logical behavior these strings and these trees are scanned in every direction. A thought is a flow of linked recollections. A recall starts from sensors. Thought may follow its process a long time after sensors triggered an initial recall. But at least from time to time sensors trigger recalls. Thought from previous recalls may interfere with new triggered recalls. A reader receives stimuli from vision sensors and they interfere with previously induced thoughts. However an explanation of a recall process shall be explained from sensors in order to remain as simple as possible.
The following process may probably work:
A bunch of stimuli is memorized in a time line. This bunch activates a node.  This node becomes a template. This template will be dropped or reinforced in the future.  All new similar records will be attached to this reinforced node. The recall process propagates both forward and backward.
- Forward from sensors to higher levels.
- Backward from higher level to sensors and actuators.
The forward propagation is a kind of hypothesis and needs confirmations by a sufficient amount of active entries. The back propagation looks like a conclusion. True or not, anyway it shall be taken into account.
If this conclusion fails, and the upper level fails too, then a new template shall appear.
The final conclusion may contain sequences of elementary actions.
A dynamic graph and a simulation  would help: more in a few days.

Thought

Forecasting future by replaying the past.

A thought is a sequential activation of linked pieces of information. Information may have been stored as crisp unique experiences. Multiple crisp past experiences have often been mixed up together into templates representing a fuzzy mean value.
From present activated stimuli or from already activated ideas, templates are activated. This activation tries and moves forward through the upper levels of memorized knowledge.
Several kind of levels shall be specified:
- Time scale levels, from tenth of seconds to hours.
- Association levels, from local sensor groups to large associations.
- Abstraction level, from crisp automatic response to philosophy.
In order to reach the upper levels, this activation needs support from the lower levels. Several rules shall drive this activation process.
- A node is activated if a sufficient amount of its inputs are activated.
- An activated node may include non-activated inputs.
- Non-activated inputs in an activated node represent predictions or actions to be done.

A thought is triggered either by a stimuli or by another thought.
A thought is driven by past experience.

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.