Discover concept correlations routine

Purpose

Discover concept correlations routine searches for correlations between concepts and updates correspondent cause-effect relations.

Discover concept correlations routine is a part of learning process.

Know-How

Discover concept correlations routine assumes that simultaneously activated concepts are related to each other.

Interface

Input parameter: {cause-effect relation increment}

Every axon which links two active concepts is updated in accordance with this formula:

new coherence attribute value =
= old coherence attribute value +
+ {cause-effect axon increment}

Implementation

1) Look at short memory.

2) Add {cause-effect axon increment} value to the coherence attribute value of the correspondent cause-effect relation.

Scenarios

1) Basic plain text parsing routine – run the discover concept correlations routine after a portion of text is loaded into short memory.

2) Every thought may end up with discover concept correlations routine.

3) Any softcoded routine may call discover concept correlations routine if necessary.

4) Deliberation routine may call discover concept correlations routine if necessary.

Example

Let’s imagine that basic plain text parsing routine reads this text into short memory: “The Sun is a medium-sized star”. Let {cause-effect axon increment} = 10.

Then discover concept correlations routine creates (or updates coherence attribute) these cause-effect relations:

“the” -> “sun”

“sun” -> “the”

“sun” -> “is”

“is” -> “sun”

.....

“sun” -> “star”

“star” -> “sun”

“the” -> “star”

“star” -> “the”

Coherence field of a newly created relation (from this list) will be set to 10.

Coherence field of already created relation (from this list) will be increased by 10.