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Table 1 clarifies the need for new method of representation and validating knowledge in
DSS. Although the logic inconsistency and dead decision problems are cleared in variability
representation methods, some works in literature expressed these problems in a DSS [27].
3. Knowledge representation in DSS using variability
In this section, we defined variability is DSS, and then described how to represent variability
in a DSS using First Order Logic (FOL).
3.1 Define variability in DSS
By variability in DSS, we mean choices representation (considering dependency constraint
rules between them). In the choice phase, the decision maker chooses a solution to the
problem or opportunity. DSS help by reminding the decision maker what methods of choice
are appropriate for the problem and help by organizing and presenting the information [7].
Hale [11] states that "relationships between knowledge objects such as kind-of, and part-of
become more important than the term itself”. We can look for choices as knowledge objects.
The proposed method defines and deals with these types of relationship and with
dependency constraints between choices such as require and exclude.
There is no standard variability representation for a DSS [21]. The proposed method can
enhance both readability and clarity in representation of variability in DSS. Consequently,
the proposed method represents variability in high degree of visualization (using graph
representation) considering the constraints between choices. As we mentioned before in the
definition of variability, there are two items: variability subject and variability object. We
suggest variability subject as a decision point and variability object and a choice . As example
from figure 1: the variability subject “Promotion” identifies a decision point . Examples of
variability objects for this variability subject are “Experience, Qualifications, or Special
Report”. This example illustrated three choices: “Experience, Qualifications, or Special
Report” that the decision maker can select from in the decision point “Promotion”.
A reward system as an example:
Rewards systems can range from simple systems to sophisticated ones in which there are
many alternatives. It is closely related to performance management. Both rewarding and
performance measuring are difficult tasks due to the decision variability that takes place in
different activities of human resources cycle as it can be seen in figure 1.
3.2 Representing variability in DSS using first order logic
In this sub-section, the notations of the proposed method are explained. Syntaxes and
semantics (the most important factors for knowledge representation methods) for the
proposed method are defined. The proposed methods composed of two layers. The upper
layer is a graphical diagram. Providing visual picture is the function of the upper layer. The
lower layer is a representation of the upper layer in forms of predicates. Providing a
reasoning tool is the aim of the lower layer. You can imagine the upper layer as a user-
interface while the lower layer as a source code. In the lower layer, decision point, choices,
and constraint dependency rules are represented using predicates. The output of this
process is a complete modeling of variability in DSS as knowledge- based. In other words,
this process creates a decision-repository based on two layers. This decision-repository
contains all decisions (choices) grouped by decision points. The proposed method validates
both decision-repository and decision making process.
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