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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








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







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.



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