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Summing is produced on all possible values in the point
x
.
Then for the presented list of considered example predictions there are:
Quan(R3) = (2/2 + 3/2 + 3/2) = 4
Quan(R1) = (2/2 + 3/2 + 3/2) = 4
Quan(O6) = (2/2) + (2/2) = 2
Quan(O5) = (1) + (2/2 + 5/2) = 4.5
Quan(O4) = (2/2) + (2/2) = 2
Quan(O3) = (1) + (1) = 2
Quan(O2) = (2/2) + (1 + 6/2) = 5
Quan(O1) = (1) + (1) = 2
Points with the greatest value of the function
Quan
(
x
) have the greatest priority of a choice.
We will call the given method of choosing a measurement point as SEH (Supporting
Environment Heuristics).
In our example the point giving the greatest information quantity is O2.
7.3 Knowledge about the sets of inconsistent environment
As a result of each measurement there is a confirmation or refutation of some prediction.
The environments
E
1
,E
2
,...,E
m
of refuted prediction form the set
Nogood
=
{E
1
, E
2
,...,E
m
}.
It can
be used for directional searching for more precise definition what kind of components from
Nogood
is broken.
Let we have the set
Nogood
= {{
And2
,
Invl
}, {
And2
,
Inv2
,
Inv5
}, {
And2
,
Inv3
}}.
Obviously the more of the components from
Nogood
are specified by measuring a value in
some device point, the more the information about which components of
Nogood
are broken
will be obtained. For using this possibility, it is necessary to take the intersection of each
environment from
Envs(x)
with each set from
Nogood
:
Envs
(
x
)
Nogood
= {
A
B
:
A
Envs
(
x
),
B
Nogood
}.
Continuing the example at Fig. 9, we obtain the following:
<R3, {{Invl}, {And2}, {And2,Inv5}}>
<R1, {{Inv3}, {And2}, {And2,Inv5}}>
<06, {{And2}, {Inv3}}>
<05, {{And2}, {Inv2,Inv5}, {Invl}, {Inv3}}>
<04, {{And2},{Invl}}>
<03, {{And2},{Inv3}}>
<02, {{And2}, {And2,Inv5}, {Inv2}, {Invl}, {Inv5}, {Inv3}}>
<01, {{And2}, {Invl}}>
For this approach the equation (1) can be changed as follows:
|
|
J
j
QuanN x
()
| |
J
i
2
J
Envs x
()
Nogood
J
Envs x
()
Nogood
i
j
||1
J
||1
J
i
j
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