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Explanation
This process defines the source of error that might occur when the new choice is selected. To
evaluate the scalability of this operation, we define additional parameter, the predicate
select(C): where C is random choice. This predicate simulates decision maker selection.
Number of select predicate (defined as a percentage of number of choices) is added to the
knowledge-based for each experiment, and the choice C is defined randomly (within scope
of choices). Figure 3 illustrates the average execution time. The output of each experiment is
a result file containing the selected choices and the directive messages.
Logical Inconsistency-Detection:
Figure 4 illustrates the average execution time to detect
inconsistency in FM Range from 1000 to 20,000 choices
Fig. 4. Logical Inconsistency Detection
6. Conclusion and future work
Representing knowledge objects and the relation between them is the main issues of the
modern knowledge representation techniques. We suggest variability for representing
knowledge objects in DSS. By introducing variability to represent knowledge in DSS we can
get both formalized knowledge representation in decision repository and support decision-
making process by validation operations. Decision selection processes are validated using
constraint dependency rules, propagation and delete cascade, and explanation and
corrective recommendation operations. Decision repository is validated by detecting logical
inconsistency and dead choices. In [5] it states, “developing and using a mathematical
model in a DSS, a decision maker can overcome many knowledge-based errors”. For this
reason, the proposed method is supported by FOL rules.
We plan to test and validate this work using real data and real life case studies from
industry. In addition, new operations are needed to validate DSS.
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