Cause-Effect relation
Cause-effect relation is a relation
between cause-concept and effect-concept.
Cause-effect relation is represented in the main memory
by cause-effect relation table.
Example:
“Sun” is a
cause for “heat”.
“Fire” is a
cause for “heat”.
“Sun” is a
cause for “sunburn”.
So, there
are 3 cause-effect relations in this
example:
{Sun->heat}
{Fire->heat}
{Sun->sunburn}
See
large-scale model here: Cause-effect graph
Why are cause-effect
relations so important?
Cause-effect relations are so important because:
1) Cause-effect relations help to understand
what would happen as a result of current situation. Cause effect relations help
to predict the future of current context.
In order to
find out what would happen, strong AI should just find all effect concepts for specified concepts.
2) Cause-effect relations help to
understand what strong AI can do in
order to achieve some goals.
In order to
figure out what to do, strong AI should just find cause concepts for the specified goal-concepts (sub goals).
Example (based on diagram above):
1) Let imagine that strong AI wants to find out
what would be the result of the sun. In order to figure that out, strong AI
would take a look into cause-effect
relations and find out that probable results are “Heat” and “SunBurn”.
2) Let’s imagine that current goal of strong AI
is “Heat”. In order to achieve this goal strong AI should follow cause-effect relation in reverse
direction and find out that “Fire” and “Sun” concepts could help to achieve the
current goal “Heat”.