|Short e-paper .
Maria Gadomski, ENEA's e-paper,
page since 1999, at present on the
interrelations (cognitive IPK architecture)
proposal of universal paradigms for Knowledge Engineering & Management
and, for necessary for it, intelligent agent modeling,
begins at the cognitive IPK model
considered as Universal
Reasoning Architecture Paradigm (URAP). It is funded on the (goal-oriented)
selection, re-definition and systemic
socio-cognitive application of a few canonical terms
which enable to define the generic kernel of every symbolic reasoning
The main original idea relies on the
of well known data processing and calculation algorithms conceptual frame
where, for an assumed application domain D, an algorithm or another
processing expression is considered a knowledge, data are
information and individual preference relations are used for the
choice of a proper knowledge expression. It means, in the case of
models/algorithms in physics we use the following calculation frame:
Information' D= KnowledgeD(
which is valid in a domain D where
the choice of the KnowledgeD depends on the
objective/goal/purpose of the intervention of a
physicist/mathematician/engineer, it means on his/her preferences related to the
state of D.
Preferences, Knowledge) conceptualization was mentioned in economy
problems in an intuitive and very synthetic manner by F.A. Hayek:
“If we possess all the
relevant information, if we can start out from a given system of preferences,
and if we command
complete knowledge of available means, the
problem which remains is purely one of logic.” (
– It means, the “logic” is the way how to use them together, and, we may
add, the efficacy of IPK processing is exactly the fundamental “task” of
intelligence (Gadomski, 1993).
the other hand, the modeling of cognitive decision-making has been latter
obscured rather than illuminated by many of subsequent refinements of cognitive
theory, where usually some arbitrarily chosen cognitive properties are
abstracted from complex contexts without sufficient argumentation and
aggregated to the form of numerous partial and reciprocally independent models.
Hayek, The Use of Knowledge in Society , The American Economic Review, Vol. 35,
No. 4 (Sep., 1945), pp. 519-530.
Definitions of the basic concepts of the IPK
to the TOGA
standard ( see also socio-cognitive
engineering paradigms), are the essential part of the assumptions of the TOGA
meta-ontology and epistemology
(Gadomski, 1993) and can be considered
everything what is/can be processed/transformed in computational and
Concept data is included in the
ontology of "elaborators", such as developers
programmers and other computation service people.
In this sense, data is a relative term and exists only in the couple (data, processing)
I: data which represent a specific property of the domain of human or artificial
activity (such as: addresses , tel. numbers, encyclopedic data, various
lists of names and
results of measurements).
Every information has always a source domain. It is a relative concept.
Information is a concept from the ontology of modeler/(problem solver)/decision-maker.
K: every abstract property of human/artificial agent which has ability
a (quantitatively / qualitatively) information into other information or
in another knowledge.
It can be: instructions, emergency procedures,
exploitation/user manuals, scientific materials,
and theories, as well as learning and teaching
methods and methodologies.
P: an ordered relation among two properties of the domain of activity of a
it indicates a property with higher utility. Preference relations serve
to establish an intervention
goal of an
Cognitive preferences are relative .
An agent ( preferences agent) which manages preferences of an intelligent
agent A, can be an external
agent or the internal part of the agent A.. Reasoning/inference on
preferences are critical for criteria
building/choice in a cognitive decision-making.
Every knowledge has its reference domain where it is applicable.
It has to include the source
domain of the processed information. It is a relative concept.
G: a hypothetical state of the domain of activity which has maximal utility in a current situation.
Goal serves to the choice and activates proper knowledge
which process new information.
a passive carrier of Knowledge, Information and/or Preferences
structures), comprehensive for humans, and it has to be recognized as valid
and useful by
one or more human organizations, it can be physical or electronic.
- from the modelers and decision-makers perspective: an active carrier
of different structures
of knowledge expressed in computer languages and usually focused
on the realization of predefined
objectives (its design-goal). It may include build-in preferences
and information and/or request
specific IPK as data.
- from the software engineers perspective: a data-processing tool ( more
precise technical def. you
may find on the Web).
The above presented concepts consist of a computational entity
discussed more in deep in
the white paper: Personoids Paradigms;
Utility and efficacy of the IPK bases depend on their many meta-properties,
such as: wisdom
(TOGA definition), possible property of a preferences
system, it is a meta-attribute of the set
of preferences enabling high efficacy in the achiving of preselected goals in the preselected
of domains-of-activity of an intelligent agent.
(as an indicator)
has a qualitative value domain, its study are included in such domains as
In the case of artificial intelligent tools, it is investigated by
Beliefs represent user's relation to particular data. They can
relate to information, knowledge
or preferences when they are used without sufficient
verification or validation.
Belief is a meta-attribute, therefore it represents relative point of view
on the contens of IPK bases ,
and depends on the particular/current observer's IPK (an intelligent
agent too) .
For example, in a concrete situation, what is a belief for an intelligent agent
A can be validated
knowledge for an intelligent agent B.
In the real world situations, in
every I, P and K entity has the certainty attribute,
and if its value is not sufficiently high but the entity is used by
decision-maker then it can be also
called his/her/its belief.
I: Information and knowledge are relative concepts and what is
called knowledge can be considered
as information on a higher, more abstract meta-activity
level (meta-reasoning level).
II: The IPK ontology requires a generic assumption on the
existence of an
abstract intelligent agent
Intelligent Agent, 2,
Intelligent Agents newsgroup) and its/his/her domain of activity
III: From the TOGA's intelligent agent centered perspective, information,
preferences and knowledge
are only comprehensive if they are related to the agent's
domain-of-activity before pre-selected.
The IPK ontology framework
consists of the
of personoids architecture.
another similar approaches
- In less formal than TOGA theories (recently under continuous development),
such as: "Information, Knowledge,
Wisdom" and "Data, Knowledge, Wisdom" (see Web)
the concepts of information and knowledge are not
sufficiently computationally defined, and wisdom is also
used in a metaphoric sense.
wisdom is a specific property of the preferences rules
of an intelligent agent ( see above).
- We may expect that intuitive conceptualization frameworks as: (Intelligence,
(Knowledge, Intelligence, Wisdom) or ( DIKW
) will be slowly transformed (2004) to the form of the TOGA's
architecture/ontology (maybe with other names but with the
same definitions - unfortunately, people
By the way, the same evolution process is seen
in the numerous variants of the BDI
(Believe, Desire, Intention)
of various cognitive agents (see Web).
Another similar approach, developed independently, in parallel to TOGA,
is SOAR (proposed by Allan Newell as a base
for his General Intelligence). It is now an advanced AI development environment
but the concepts of
are not clearly distinguished (2004).
- It is yet
useful to notice that for software system developers which
start from before developed models, quasi all
problem specification information are considered as data, therefore the IPK conceptualization is not well visible from
such technical perspective.
More about meta-ontological
and epistemological TOGA assumptions:
( Nov. 16, 2004): Information,
Preferences, Knowledge 2.250.000 docs. 1st & 2nd
from MKEM server
Sept. 30, 2005): Information,
Preferences , Knowledge 17.100.000 docs. 1st & 2nd
( Sept. 30, 2005): Information,
Preferences 103.000.000 docs. 1st is this page.
All comments are welcome.
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