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APC1 – On
enumeration by the human of the cell values over an event class

APC2–
Minimal Voting Procedure

APC3 – Using the
MVP to rout information

APC4 – Using
eventChemistry to improve the framework specification

**Action Perception Cycles 3**

** **

**Using
the MVP to rout information**

** **

** **

The instantiation of a Sowa-Ballard framework for each of a series of
events lead to a categorical abstraction about the nature of the cell in the
context of the slots of events in the domain space

{ E_{
l} | l = 1, . . . , 100 }

{ F_{l} | l = 1, . . . , 100
} à { { a(i) _{l }}
_{i} | i = 1, . . . , 18 }

The Sowa-Ballard Framework is derived from:

{ independent, relative,
mediating }

{ physical, abstract }

From each of the slots (e.g.,
framework cells) we create categories around those values which are the same,
or closely similar. The issue of
similarity must be formally handled with a thesaurus so that if strings that
are not equal are to be treated as equal, for the purpose of the
categorization; then this is well documented.
We might reduce the size of the set of
{ a(i) _{l }} first in the very nature way in which exact equality will
reduce the size of a set so that

{ a, a } = { a }

In doing this, one might record
the frequency of the value as an occurrence in the slot. Due to repetition, the set of values will
often be less than the number of events.
One might reduce the set of categories further using a thesaurus. The result of this process of reduction
produces categorical abstraction atoms for each slot. The notation for the categorical abstraction atoms is made by
introducing a prime symbol, so that a’(i) is used for the derived slot atoms
and a(i) is used to indicate the original cell value. It is appropriate to talk about the reification of slot atoms.

Now, following the original
notation for the Minimal Voting
Procedure we have, for each in a series of events

Domain space = { E_{ l} | l = 1, . . . , 100 },

the set of categories **C** = { C_{q} }
is predefined, initially, and associated with the names of the event types.

**C = **{
a(0) _{l }} = { a(0) _{l }| l = 1, . . . , 100 } **= **{ a’(0) _{g }| g = 1, . . . , q < or = 100
}

Where the prime mark “ ’ ” in a’(0)
indicates that the set { a(0) _{l }} has been reduced using similarity analysis (see for example the
work by Prueitt on declassification
similarity engine).

For each of the events we produce a representational
set for the event using a Framework, such as the Zachman or Sowa-Ballard. For specificity let us assume that we are
using the 18-element Sowa-Ballard Framework.
Over the domain space, assuming 100 events, we have:

Domain space à { <
a(0), a(1), a(2), . . . , a(18) > _{l }| l = 1, . . . , 100 }

In the Minimal
Voting Procedure notation, objects

**O **= { O_{1} , O_{2} , . . . , O_{m}
}

can be documents,
semantic passages that are discontinuously expressed in the text of documents,
or other classes of objects, such as electromagnetic events, or the
coefficients of spectral transforms.
Here we take the objects to be events and m to be 100.

Some
representational procedure is used to compute an "observation" D_{r}
about the events. The subscript r is used to remind us that various types of
observations are possible and that each of these may result in a different
representational set.

We use the
following notion to indicate the observation using a Sowa-Ballard Framework:

D_{r }: E_{i} à { a(0), a(1), a(2), . . . , a(18) }

This notion is read
"the observation D_{r} of the event E_{i} produces the
representational set { a(0),
a(1), a(2), . . . , a(18) }

We now combine
these event representations to form category representations.

· each
"observation", D_{r}, of the event has a "set" of
cell values

D_{r} : E_{k}
à **T**_{k
}= { a(0), a(1), a(2), .
. . , a(18) }

· Let **A** be the union of
the individual event representational sets **T**_{k}.

**A **= È **T**_{k}.

One can talk about
slot entanglement in various ways. If S(i)
and S(j) are two slots and q is a slot atom in both slots, then a SLIP reading of the
membership records for S(i) and S(j) will produce the categoricalAbstraction
atoms s(i) and s(j) with the “relationship” between the two slots given as the
slot atom q. The SLIP parse of the data
will produce the relationship, called by Pospelov a syntagmatic unit,

< s(i), q, s(j) >

The
categoricalAbstraction (cA) and eventChemistry (eC) software products now (as
of September 2002) allow humans to easily see all of the entanglement between
slots, and to annotate meaning to this entanglement.

This set **A**
is the representation set for all of the slots of the framework over the domain
space.

Using an iterated
process, the humans in a community develop the category representation set, **T* _{q}**,

__ __

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