Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

A rule may also include a number of abnormal predicates. For
instance, one simple rule to test the capability of car driving of X is given
below.
Adult (X) ∧ Car (Y) ∧ ¬ab (X) ∧ ¬ab (Y)→Can-drive (X,Y).
We would now prefer to write ‘,’ instead of ‘∧’ in the L.H.S. of the
‘→’ operator. Thus the above rule can be expressed as
Adult (X), Car(Y), ¬ab(X), ¬ ab(Y) → Can-drive(X,Y).
In this section, we would use a closed-world model for all predicates,
except the ab predicates and postpone the commitment of the closed-world
model for the ab predicates until we do not see other evidences [1]. We now
illustrate the above principle by an example. Consider the following
knowledge base.
1. Tall persons are normally fast-runners.
2. Diseased persons are normally not fast-runners.
3. John is a tall person.
4. John is diseased.
The above pieces of facts and knowledge are represented below in nonmonotonic
logic using ab predicates.
1. Tall(X), ¬ab1(X)→ Fast-runner(X).
2. Diseased (X), ¬ ab2(X) → ¬ Fast-runner(X).
3. Tall (john).
4. Diseased (john)
Now, postponing commitment to ab1 and ab2 predicates, we by
resolution principle find
¬ ab1 (john) → Fast-runner (john)
¬ ab2 (john) → ¬ Fast-runner (john).
Now, unless we know anything about ¬ab1(john) and ¬ab2 (john), these
two contingent facts are not inconsistent. In fact, we would assume ab1 and
ab2 to be false, unless we discover anything about them. But, it is to be noted
that setting ab1 and ab2 false yields a contradiction Fast-runner (john) and ¬
Fast-runner (john). To solve this problem, we have to rank the level of ab1 and
ab2. Suppose, we assume ab2 is more likely to be false than ab1, then we can
infer ¬Fast-runner (john) is more likely to be true than Fast-runner (john).
We now define a strategy for redundancy checking.