Rules

If an AI knows the value of speaker.residence.city, then it should be able to derive the value of speaker.residence.location. ie. Generalize.

This represents a whole area of research: How do we give an AI with some knowledge about the world the ability to deduce other things?

One strategy is to employ rules.

Rules are like transformations in that they have an input specification, called the conditions, and an output specification, called the implication.

Conditions:

$x is_a residence
$x.city = $y

Implication:

$x.location = $y

What this says is that if we have an entity in our data set that is a residence, and its city attribute has a value, then we can deduce more generally the location of that residence as being the same.

Rules are used in reverse, however: We start with the problem, "How can I deduce speaker.residence.location", and search for rules that can imply that information. When we find one, we evaluate whether the we can meet the conditions necessary to make the implication.

Textual representation

rule: $x is_a residence & $x.city = $y -> $x.location = $y

Alternate form:

rule:
  $x is_a residence
  $x.city = $y
  -> $x.location = $y