(***) (e-mail Dr. Prueitt) (***)
Technical
Descriptions
generalFramework (gF) theory
(September 2002)
categoricalAbstraction (cA)/
eventChemistry (eC) tutorial
(August 2002)
KOS and the mapping of the
emergence of mental/social events (August 2002)
root-KOS and I-RIBs (December 2001)
Rural American Safe Net Proposal
The Anticipation of Events in a Fish Pond
Dr. Paul Stephen Prueitt
September 16, 2002
Contents
Use of generalFramework Theory
Notation for generalFrameworks
Economic implications from improvements in aquatic systems
control technology
Advanced architectural notions
On event specification at two levels
Introduction
We are interested in the description and measurement of events in a small fish pond. A tri-level “semiotic” system is evolved as an interactive knowledgebase. Our interest in events that occur in a pond is as an example of any event space within a natural environment having some degree of instrumentation and allowing a certain degree of control.
The designation “tri-level” is used to capture what in complexity theory is a characteristic of any natural system such as a pond. The tri-level architecture is using in text analysis as a means to create subject matter indicator taxonomy. We call the set of “indicators” a knowledge base because the description of the events in the natural system is assisted through the use of the indicators, both as a means to measure what is happening and as a means to predict consequences.
Within the elements of this knowledgebase are
1) Atoms of invariance from micro-event logs (derived from physical sensors located in the water, ground, or air environment.)
2) Rules of event aggregation expressed as first order predicate logic
3) EventCompounds and both visual and auditory rendering of these events
4) Top down expectations on event formations expressed as stochastic formation (using some elegant mathematical notions that can be explained but which take some time to explain).
Dr. Paul Prueitt has developed architecture for knowledge vetting within communities, initially directed at a cyber event knowledge base. Our interests are in building commercial systems to account for knowledge flows within communities. The technology has knowledge management components, such as best practices and lessons learned; however there are also deep innovations in knowledgebase design. These innovations are described from the four “Technical Description” links found at the top of this URL page.
Pictures of Aquaculture Farms
Four lower photos courtesy of Jimmy L. Avery, MSU
We could use aquatic science to compose rules of event aggregation and we will use stochastic formulation to empirically derive situational anticipation of the specific pond. The notion of “being situated” is extended to suggest that optimal performance of the pond depends not only on general rules of event aggregation, but also specific physical properties that may be non-stationary and very difficult to predict from general principles of chemistry and biology. A specific variation in key substances is an example.
Use of generalFramework
Theory
We could use a generalFramework that is derived from the Zachman Framework. However, we might as well use several different frameworks as we develop this technology. We envision a pond controller system to be composed of a number of physical sensors, a small inexpensive computer and the Orb technology. The underlying technology will be immediately derived from and expository of the notional papers which have been made public domain.
One framework that we might use is the following
Interrogatives = { why, who, what, where, when, how }
Causes = { material, reflective, formal }
Status = { absence, presence }
Please review the foundational paper of
generalFrameworks (gF) and
eventChemistry (eC)
The Fish Pond Framework creates a 6*3*2 matrix of cells. Each cell evokes a question that can be answered, or not, as a process of describing a specific event. Over time, the values in the same cell begin to have repetitions. As these repetitions are found they can be viewed using the existing Event browser.
There are many innovations in the approach that we make. So we will develop a description of the proposed application of Synthetic Intelligence to pond controllers. The description is intended to be a thought experiment, since if we actually had the opportunity to develop this technology, we would immediately be able to rely on experience from those who have been at the aquaculture business.
The first step is to develop a description of the questions that are related to each of the 36 cells in the framework. The OSI Framework browser is to be completed during the month of September 2002, and a tutorial on this browser will be made available.
The questions are developed using a type of minimalism that is discussion in a PowerPoint presentation made by Prueitt in 2000 at the e-Gov conference.
Why ---
reasons about aspects of the event
Who --- fish, micro-organisms, living subsystems within the pond
What --- food, temperature, water flow, substances, waste,
Where --- earth, water, weather, phase of metabolic cycle
When --- before, after, later
How --- fast, slow, towards health, away from health
Material --- the physical substances involved
Reflective --- the autopoietic (system) envelope and how this is being expressed
Formal --- what is the science on this situation
Absence --- the question is modified to reflect absence
Presence --- the question if modified to reflect presence
This pond Framework (pF) reflects the anticipation that adding and subtracting substances is the proper way to control a fish pond.
Questions are developed as descriptive. The most minimal form is the set of three phrases:
Interrogative, cause, status
For example the pF cell (1,1,1) is Why, Material, Absence.
The Interrogatives are fully spelled and Material and Status abbreviated by the first letter. So
Why, Material, Absence à Why–M–A
The first 18 questions are:
Why-M-A why is the material needed
Why-M-P why is the material in excess
Why-R-A why is processes not sustaining
Why-R-P why is process sustaining
Why-F-A why does the model not predict
Why-F-P why does the model predict
Who-M-A which active agents are missing
Who-M-P which active agents are present
Who-R-A which processes are not active
Who-R-P which processes are active
Who-F-A is technical assistance needed
Who-F-P has technical assistance been given
What-M-A what inactive agents are missing
What-M-P what inactive agents are present
What-R-A is background environment unstable
What-R-P is background environment stable
What-F-A do we understand this
What-F-P what is our understanding
The other 18 follow this pattern.
Notation for
generalFrameworks
We now introduce some of the notation as consistent with the notation using in general Framework theory. We recognize that the notation is very general and that application to the specific chemistry and biology in fish ponds will require domain expertise that our group does not as yet have. We are looking for a research and development partnership.
The
instantiation of a 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 having, say, 1000 events under
analysis.
{ E l | l = 1, .
. . , 1000 }
{ El | l = 1, . .
. , 1000 } à { { a(i) l | l =
1, . . . , 1000} i | i = 1,
. . . , 36 } =
{ Cq | q = 1, . . . , 36 } = C
C
is a
collection of 36 slots, one slot existing for each cell in the
framework. The cells may be filled
automatically with readings from various instrumentation and laboratory
analysis. The analysis will be on the correlation between types of data
(categorized into ranges of data values) and conceptual representation of
textual responses from human dialog (typing or voice to text
transcription).
From each of the slots (e.g., framework cells) we create categories around those values (conceptual and numeric) which are the same, or closely similar. From this we look at the categoricalAbstraction/eventChemistry structure using the OSI browser and construct both ontology and situational logics to express theories for event causation.
With the textual information, the issue of conceptual similarity must be formally handled with a thesaurus. Strings that are not equal are to be treated as equal, for the purpose of the categorization.
In this way, we might reduce the size
of each of the sets in the collection, of sets, { a(i) l } first in the very natural way in which
exact equality will reduce the size of a set.
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
With the numerical data we record the frequency of values falling within a window as an occurrence in the slot. How these windows are defined is a separate issue that is handled using analytic methods.
Due to repetition, the set of cell values will often be less than the number of events. We use the term “slot” to indicate the set of all values that have been placed into a specific cell.
The notation for 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. This
produces a minimal number of categories within each slot that characterize the
repetition of values and similarity analysis.
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,
. . . , 1000 },
The set C0 is predefined,
initially, and associated with the names of the event types.
C0 = {
a(0) l } = { a(0) l | l = 1, . . . , 1000
} = {
a’(0) g | g = 1, . .
. , q < or = 1000 }
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. Over the domain space, assuming 1000 events,
we have:
Domain space à { <
a(0), a(1), a(2), . . . , a(36) > l | l = 1, . . . , 1000 }
In the Minimal
Voting Procedure notation, objects
O = { O1
, O2 , . . . , Om }
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 1000.
Some
representational procedure is used to compute an "observation" Dr
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 Framework:
Dr :
Ei à { a(0), a(1), a(2), . . . , a(36) }
This notion is
read "the observation Dr of the event Ei produces
the representational set { a(0),
a(1), a(2), . . . , a(36) }
We now combine
these event representations to form category representations.
·
· each
"observation", Dr, of the event has a "set" of
cell values
Dr :
Ek à Tk
= { a(0), a(1), a(2),
. . . , a(36) }
·
· Let A be
the union of the individual event representational sets Tk.
A = È Tk.
Now we can talk
about slot to 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, is defined for each category
number q.
Economic implications from improvements in aquatic systems control technology
This section is still under development. We are developing research and economic relationship with some existing aquatic production organizations.
Pictures from the Philippines
Advanced architectural
notions
Using Dr. Prueitt’s work as a foundation technology,
we have proposed a knowledgebase technology to study state transition, in real
time in the context of processes that occur in ponds. Existing instrumentation detects certain categories of state
transitions. Colleagues of Prueitt have
long felt that game theory might be altered to reflect the type of axiomatics
openness that both the minimal voting procedure and
Russian QAT has. We would
like to apply this notational and logic R&D to the practice aspects of
governing a small commercial aquaculture system. We are looking for domain experts who wish to partner in the
development of this technology. Please
contact OntologyStream Inc.
In the context of open game theory, we posit that
there may be an alphabet of fundamental "content-type" states, so
that one could always, almost always, say that a state transition was from
content-type a to content-type b via a
transition-type q. The object
of the game theory is to predict the expression of
chemical/biological/ecological structural coupling by providing visual and
instrumental clues, anticipating state transition of specific type, and
modeling the future states. This is a
big task, but many different groups have completed much of the preliminary
work. It is a question of bringing some
of the best minds together as advisors to a small core group of technology
developers.
The Ontologically Relative
Stratification having different locations
In theory, one can see the content states a
and b as aggregations from a small set of substructural elements
- derived from the invariance across a number of content occurrences. So that
a = f(A) and b
= f(B)
where A and B are subsets (bags) from this small set
of substructural "content" elements.
The discovery of this small set of event atoms allows eventChemistry
(eC) to be expressed with the aid of human perceptual acuity. As the visual rendering occurs human
analysts are in a position to take full benefit from private tacit knowledge
and shared community knowledge. A senseMaking process is enabled.
In reference to the Figure: “The Ontologically
Relative Stratification having different locations” we might find that
Sa = A and Sb
= B.
However, outside of the pragmatics of a specific
situation, one cannot “know” that A and B are to be necessarily causally
related in a real situation that has not yet occurred. To show that Sa = A and Sb
= B are causally related in a single specific situation is not often a small
matter for many reasons. The only way
(again by hypothesis) to measure A or B is indirectly by observation of the
behavior of content states a and b. But most often, in stratified systems, the
measurement itself induces change in A and B and f due to the cross scale
aspect of the measurement.
Because of the complexity of any living ecosystem,
it is absolutely necessary that human cognitive acuity be allowed to make sense
of formative eC in real time. To
completely automate the process is to set the potential for catastrophic
failures now and then.
Some of the event atoms from
a study of computer port access
In our work on formative ontology
the bags from substructure become relative to location and are subject to “top
down” constraint that has built up over time in the reaction (production)
chains at the various levels of the stratification. So, over any period of time, we have a small finite state
machine as a model of natural ontology.
Using the SLIP Browsers we
built and have access to any arbitrarily defined small finite state machine
that is open to the occurrence of new states depending on human perception of
the categorical invariance in the data. So we are already far into the
challenge of demonstrating in practice what we feel we see from formalism and
theory.
Once the events are modeled by cA/eC formalism and detectable
by physical and lab instrumentation, we will be in a position to present states
from the computer tri-level architecture to the human and measure whether
1)
the
state is known,
2)
the
state is unknown
3)
the
state is not recognized as being known or unknown
and then further examine state transitions if there
are subtypes of
1)
recognition
2)
puzzlement
3)
planning
These subtypes of transition may be sufficient to
begin the scientific study of human interactions with small ecosystems such as
commercial fish ponds.
On event specification at
two levels
Let
us look again at the notation:
Domain space à { < a(0), a(1), a(2), . . . , a(36) > l | l = 1, . . . , 1000 }
This notation is further developed in the
Foundational Paper on generalFramework (gF) Theory. We call this a 36 tuple because the a(0) is
a specification of event type. The
other values in the n-tuple are from a theory of kind that is constructed from a
substructural decomposition of the events within context.
{ El | l = 1, . .
. , 1000 } à { { a(i) l | l =
1, . . . , 1000} i | i = 1,
. . . , 36 } =
{ Cq | q = 1, . . . , 36 } = C
Or in words, 1000 events are observed (by humans and
instrumentation) to produce an instantiation of the 6*3*2 pond dynamics
framework. In each of the cells { a(k) l
} k , k = 1, …,36, are placed information, sometimes blank,
and this information is then compressed into a theory of type { a’(k) l }
k
{ { a(k) l | l =
1, . . . , 1000} i | k = 1,
. . . , 36 } is a set of sets
Patterns of slot values within context of event
types are then composed into first order logics, perhaps in the form of expert
systems. However, the objects of
investigation are not always formal objects such as found in computer
systems. So a QAT architecture opens this logic up to
real time perturbations of various forms.
I am changing the index notation from “i” to “k”
just to help make the nature of these sets clear. The notion of a theory of type is a concept that has various
degrees of manifestation, including NLP derived ontology, and a scholarly
literature existing within perceptual physics and cognitive science.
Each cell of the framework produces a slot, as in
scripts with slots and fillers, and the theory of type is the set of fillers
that are found in the compressed “knowledge base”. The relationship between fillers in different slots is a key part
of the automated “theorem proving” that we derive with the minimal voting procedure
and QAT.
(***) (e-mail Dr. Prueitt) (***)