P&R is characterized as a parametric design task whose goal is to assign values to each parameter in a system. The select parameter inference chooses one parameter among all the parameters to compute its value. Figure 3 shows the inference with its knowledge roles: INPUT PARAMETERS, PARAMETER DEPENDENCY RELATIONS and SELECTED PARAMETER.
The INPUT PARAMETERS must be supplied by the user before the system is configured. In the VT task, the input parameter values provide the basic characteristics of the elevator system such as door opening type (center, for doors which open in the center; side, for doors which open to one side of the car), car capacity (the maximum occupant weight that the system is able to support) and so on. The input parameter values are used as a starting point for computing the values of the other parameters.
The PARAMETER DEPENDENCY RELATIONS define how the values of some parameters depend on the values of other parameters to be computed. In the VT task, for example, the sling underbeam is equal to the car cab height plus the sling underbeam space. This formula expresses that the sling underbeam depends on the car cab height and the sling underbeam space. The dependency relations among the parameters are a fundamental aspect of the knowledge structure for Propose&Revise. Here, instead of a hierarchical structure of components and subcomponents, the parameters are organized in a network that models the interdependencies among parameters. Thus, to use Propose&Revise, the application domain must present or must be mapped to the same organization. In this way, some assumptions are made about the dependency relations among parameters:
In addition, it cannot exist cycles in the dependency relations:
Figure 5: The formalization of the
select parameter inference and its knowledge roles.
The PARAMETER VALUES are supplied by the user for the input parameters, or they can be the values of the other parameters computed by the method. In P&R, each parameter has a unique value. The SELECTED PARAMETER has its value not computed yet, but all parameters on which it depends are already computed.
Figure 5 presents the formalization of the
select parameter inference and its knowledge roles. In
(1){
the class of parameters is
defined. Definition (2) associates a unique value to each
parameter. The value-cardinality of frame ontology
constrains the relation Parameter-Value in such a way
that each parameter has a unique value. In (3) the input
parameters are defined. Definition (4) expresses the direct
dependency relations among parameters. Here, an input parameter
does not depend on another parameter. In (5), the Depends-On
relations comprise the direct and indirect dependencies among
parameters. In addition, the definition makes explicit the fact
that no cycles exist in the dependency relations. The
ASYMMETRIC-RELATION and
WEAK-TRANSITIVE-RELATION are defined in
frame ontology. The ASYMMETRIC-RELATION
means that if ?p1 depends on parameter ?p2, this
implies that ?p2 does not depend on parameter ?p1,
such that there are no cycles in the direct
dependency relations. To define the indirect dependency among
parameters, the WEAK-TRANSITIVE-RELATION
says that, if a parameter ?p1 depends on parameter ?p2,
and that ?p2 depends on parameter ?p3, then
?p1 depends on ?p3. It is a ``weak'' transitive
relation because ?p1 is not equal to ?p3, which
warrants the nonexistence of cycles in the indirect dependency
relations. In (6), the output knowledge role is defined.
Definition (7) presents the select parameter inference
that chooses a parameter whose value is not computed yet, but
all parameters on which it depends are already computed.