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The OppU'&fiari
analyst, as opposed to an RV analyst, should construct such
a
list for each mission.
While there may be considerable similarities between element
lists for
different missions, undoubtedly the
lists
will require specialization.
In Section II-C below, we
show the construction of one element
list and how
it can be applied
to
a
set of 65
|
_
[
targets.
2.
(U)
Analysis of Complete Responses
|
Once an appropriate universal set of elements has been created, and fuzzy
sets that define the target and the response have been specified, the comparison between them is
straightforward. We have defined accuracy as the percent of the target material that is described
correctly by a response.
Likewise, we have defined reliability (of the viewer)
as the percent of
the response that
is correct. 4
Although
in the laboratory
it
is required to provide
a posterior
probability estimates of the target-response match,
in an
setting,
this may be
less
important.
All that
is usually necessary
is to describe the accuracy and
reliability
for complete
responses, and for individual target elements of interest.
These quantities for the y'th sessions are
2 w‘<rj n tju
k a
n
2 WkRj.*
*=
n
X wk (Rj n
Tj) k
k =
l
n
£ «,«7m
k =
l
a)
(2 )
where the sum over k
is called the sigma count in fuzzy set terminology, and
is defined as the sum
of the membership values
(ji)
for the elements of the response, the target, or their intersection,
and n
is the number of possible elements as defined by the element
list. A fuzzy intersection
is
defined as the minimum of the intersecting fuzzy set membership values.
In this version of the
definitions, we have
allowed
for
the
possibility
of weighting the membership
values,
Wk,
to
provide mission-defined relevances.
(U)
For the above calculation to be meaningful,
the membership values
for the targets
must be similar in kind to those for the responses.
For most mission-dependent specifications,
this
is generally not the
case.
The target membership values represent the degree
to which a
particular element
is characteristic of the target, and the response membership values represent
the
degree
to which
the
analyst
is
convinced
that
the
given
element
is
represented
in
the
response.
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(U)
Until RV abilities can encompass the recognition of elements as well as their degree of
target characterization, we are required to modify the target fuzzy set. An analyst must decide
upon a threshold above which an element
is considered to be completely characteristic of the
target site.
In fuzzy set theory, this is called an a-cut: a technique to apply a threshold to the p
values such that if the original value exceeds it, reassign the value to
1, otherwise set
it to 0.
In
this way, the analyst’s subjectivity can be encoded in the response fuzzy set, and Equations
1 and
2 remain valid.
3.
(U)
Analysis of an Individual Element
(U)
Equations
1 and 2 can be simplified to provide an accuracy and reliability on an
individual element basis instead for a complete response.
For example,
let N be the number of
sessions against different targets that exist in a current archive for a specified viewer.
Let e be an
element in question
(e.g., airport). Then the empirical probability that element e is in the target,
given that the viewer said
it was,
is given by
( 3 )
where Nc is the number of times that the individual was correct, and Nr
is the number of times
that element € was mentioned in the response.
R(e)
is also the reliability of the viewer, for that
specified element.
(U) To compute what chance guessing would be, we must know the occurrence rate
of element e in the N sessions.
Let N0 be the actual number of times element e was contained in
the N targets.
Then the chance-guessing empirical probability
is given by
i
Ro(e) can also be considered as the guessing reliability
(i.e., the reliability that would
be observed
if the viewer guessed e during every session).
The more R(e) > R0 (e), the more
reliable the individual
is for the specified element.
* (U) The empirical probability that th^e viewer said element e, given that
it was in the
target,
is given by
Nc_
No'
A(e)
is also the accuracy of the viewer for that specified element.
(U) As a numerical example, suppose a single viewer participated in N = 25 sessions.
Let e = “airport.”
Further suppose that N0 = 5 of the targets actually contained an airport.
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(U)
Then, R0 (airport) = 0.20
is the chance probability
(i.e.,
guessing airport during every session
would only by 20 percent reliable).
Assume that the! viewer mentioned airport Ni = 6 times and
was correct Nc - 4 times.
Then this viewer’s reliability for airports is computed as R(airport) =
0.67 > Ro(airport) = 0.20. The viewer’s accuracy fo? airports is computed as A(airport) = Nc/N0
!
= 0.80.
Thus in this example, we can conclude th?t this viewer
is reasonably accomplished at
remote viewing an airport.
j
B.
(U)
Prototype Analysis System
\
! We assume
that an
>
___
• analyst has
constructed
a mission-dependent
universal
set
of
elements.
We
further
assume
that
there
are
a
number
of
competing
interpretations of the target site
in question.
j
i
1.
(U) Target Templates
The
first step
in our prototype ’analysis system
is to define templates
(i.e.,
general descriptions of classes of target types)
of
all competing target interpretations from the
universal
set of elements.
I
'
~*
__
>
I
>
Exactly what the templates should represent
is entirely dependent upon
what kind
of information
is sought.
Both the underlying universal
set
of elements and the
templates must be constructed to be rich enough to allow for the encoding of all the information
_
That is,
if neither the set of elements nor the templates can meaningfully
represent information abouti
~t\\&
__j
< then
it will be unreasonable to consider
asking,
qucs.-lib'iS
a.bt>vA
the
site.
Furthermore,
a
certain
amount of atomization
is necessary because such division into small units provides the potential
for interactions within the universal set of elements.
If the profile of a
l
^facility consists of a
single element, the template would be useless unless the response directly stated that particular
element;
rather,
the
profile
should be
constructed from
groups
of elemental
features
_
P
\
There
are
two
different
ways
to
generate
target
templates.
The
most
straightforward technique
is also likely to be the most unreliable, because
it relies on the analyst’s
judgment
of
a
single
target
type.
With
this method,
the
analyst, who
is
familiar
with
the
intelligence
problem
at
hand,
simply
generates membership
values
for
elements
from
the
universal
set
of elements based upon
his
or her
general
knowledge.
Given
the
time and
resources, the best way to generate template membership values
is to encode known targets that
are closely related
Each template
ji
is the average value
across targets, and thus
is more
reliable.
If
it
is known that some targets are more
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