This relationship gives evidence for the presence of an object
of class , given the presence of an object of superclass S.
For example, evidence for the object being a wide-bodied aircraft lends
some support for the possibility of it being a DC-10.
The use of superclass is not rigorous
here - the notion is of a category generalization along
arbitrary lines. Hence, a class may have several generalizations:
an
may generalize to
or
.
Class generalization does not require that
all constraints on the generalization are satisfied by the specialization,
which differentiates this evidence type from the description relationship.
So, a superclass need not be a strictly visual generalization, that is,
there may be different models for the object and its superclass.
Superclasses provide circumstantial, rather than direct, evidence as the
presence of the
alone does not provide serious
evidence for the
being present.
If the object had both strong
and
evidence, the implication should be stronger.
If the object had strong
and weak
evidence, then it would be less plausible for it to be a
.
Because the superclass is a generalization, its plausibility must always be
at least as great as that of the object.
Hence, the evidence for an object can be at most the
minimum of the evidence for its superclasses.
Constraints that help specify the superclass evidence computation are:
These constraints lead to the following formal definition of the superclass evidence computation:
Given: | |
a model instance of class ![]() ![]() |
|
a set {![]() ![]() |
|
a set {![]() ![]() ![]() |
|
Then, the superclass evidence is: | |
![]() |
|
If no superclass evidence is available, this computation is not applied. The portion of the network associated with this evidence is shown in Figure 8.10, where the square unit is a "min" unit (representing a balanced tree of binary "min" units) and the inputs come from the appropriate superclass nodes.