Knowledge Representation and Validation in a Decision Support System: Introducing a Variability Modelling Technique - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 37

the selection process, i.e. validate decision-making process. At the end, the choices that
represent the final decisions are assigned by select and not( notselected ) predicates.
4. Variability validation in DSS
Although variability is proposed as a technique of knowledge representation that provides a
decision repository; validating this repository and decision making process is important.
In a decision making process, a decision maker selects the choice(s) from each decision
point. The proposed method guides the decision maker by: 1) validating the constraint
dependency rules, 2) automatically selecting (propagation and delete-cascade) decisions,
and 3) provide explanation and corrective recommendation. In addition, the proposed
method validates the decision-repository by detecting dead choices and logical
inconsistency. In this section, six operations are illustrated. These operations are
implemented using Prolog [29].
4.1 Validating the decision making process
4.1.1 Constraint dependency satisfaction
To validate the decision-making process, the proposed method triggers rules based on
constraint dependencies. Based on the constraint dependency rules, the selected choice is
either accepted or rejected. After that, the reason for rejection is given and correction actions
are suggested. When the decision maker selects a new choice, another choice(s) is/are
assigned by the select or notselect predicates . As example, in table 3: number 1, the choice x is
1. ∀ x, y: type(x, choice) ∧ type(y, choice) ∧ requires_c_c(x, y) ∧ select(x) ⟹ select(y)
2. ∀ x, y: type(x, choice) ∧ type(y, choice) ∧ excludes_c_c(x ,y) ∧ select(x) ⟹ notselect(y)
3. ∀ x, y: type(x, choice) ∧ type(y, decisionpoint) ∧ requires_c_dp(x, y) ∧ select(x) ⟹
select(y)
4. ∀ x, y: type(x, choice) ∧ type(y, decisionpoint ) ∧ excludes_c_dp(x, y) ∧ select(x) ⟹
notselect(y)
5. ∀ x, y: type(x, decisionpoint) ∧ type(y, decisionpoint ) ∧ requires_dp_dp(x, y) ∧ select(x)
⟹ select(y)
6. ∀ x, y: type(x,decisionpoint) ∧ type(y, decisionpoint) ∧ excludes_dp_dp(x, y) ∧ select(x)
⟹ notselect(y)
7. ∀ x, y: type(x, choice ) ∧ type(y, decisionpoint ) ∧ select(x) ∧ choiceof (y, x) ⟹ select(y)
8. ∃ x ∀ y:type(x, choice ) ∧ type(y, decisionpoint ) ∧ select(y) ∧ choiceof (y, x) ⟹ select(x)
9. ∀ x, y: type(x, choice ) ∧ type(y, decisionpoint ) ∧ notselect(y) ∧ choiceof (y, x) ⟹
notselect(x)
10. ∀ x, y: type(x,choice ) ∧ type(y, decisionpoint ) ∧ common(x,yes) ∧ choiceof (y, x) ∧
select(y) ⟹ select(x)
11. ∀ y: type(y, decisionpoint ) ∧ common(y) ⟹ select(y)
12. ∀ x, y: type(x, choice ) ∧ type(y, v decisionpoint ) ∧ choiceof (y, x) ∧ select(x) ⟹ sum(y,(x))
≤ max(y,z)
13. ∀ x, y: type(x, choice ) ∧ type(y, decisionpoint ) ∧ choiceof(y, x) ∧ select(x) ⟹ sum(y,(x))
≥ min(y,z)
Table 3. The main rules in the proposed method