Modeling paradigms for modeling the Ontological elements of a PMESII environment

In appendix C of Behavioral Modeling and Simulation: From Individuals to Societies there is a decent overview of some modeling techniques to capture PMESII factors.

These approaches/techniques include:
Concept Maps
Concept Graphs
Social Networks
Casual Graphs
Systems Dynamics Model
Neural Networks
Situation Theory

The link above gives a nice reference to particular appendix, or the book is available for browsing here:





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More on Topology

A few more decent references on topology representing a couple of decades worth of text books on the subject (1961, 1970, 1980).

First this is from 1961 -


Here is one from 1970 -


And finally, from 1980 -


Tags: topology

System Topology

I rely on System Topology in the same that a network engineer for an information network would refer to a Network Topology. Only I am referring to the interconnected processes, and their relations amongst each other (as captured in the IDEF series of diagrams, or more recently, by Sowa's claim that a system is much more appropriately looked at as an interconnected graph of processes, rather than a group of data/object states, that only use process as a connective tissue).

A nice little definition (from Wolfram Mathworld) -

Topology is the mathematical study of the properties that are preserved through deformations, twistings, and stretchings of objects. Tearing, however, is not allowed. A circle is topologically equivalent to an ellipse (into which it can be deformed by stretching) and a sphere is equivalent to an ellipsoid. Similarly, the set of all possible positions of the hour hand of a clock is topologically equivalent to a circle (i.e., a one-dimensional closed curve with no intersections that can be embedded in two-dimensional space), the set of all possible positions of the hour and minute hands taken together is topologically equivalent to the surface of a torus (i.e., a two-dimensional a surface that can be embedded in three-dimensional space), and the set of all possible positions of the hour, minute, and second hands taken together are topologically equivalent to a three-dimensional object.

My intention is to use the term system topology to refer to the overall graph of interconnected processes within a system, which I submit is potentially dynamic and can be potentially changed as much as the value or nature of objects affected by those processes.

A decent book (compliments of Google Books, once again) introducing the mathematics of topology . . .




Tags: system topology

Data Modeling - the quest for a good definition of Conceptual Model

Some promising results are herein:



Also Interesting:

• Data Modeling 101
  
• Introduction to Data Modeling
  
• Wikipedia entry (some decent links)
  

Tags: data modeling

Dimensions of Processes (1) - Time and Temporal Placement

The first dimension I am exploring, concerning a methodical decomposition of process, is the dimension of time and temporal placement of the process within its universe of occurrence.

First, on spatial reasoning, there is an informal introduction to the whole concept on wikipedia, of which the salient point (for automata) is:

Spatial-temporal reasoning is also studied in computer science. It aims at describing the common-sense background knowledge on which our human perspective on the physical reality is based. Methodologically, qualitative constraint calculi restrict the vocabulary of rich mathematical theories dealing with temporal or spatial entities such that specific aspects of these theories can be treated within decidable fragments with simple qualitative (non-metric) languages. Contrary to mathematical or physical theories about space and time, qualitative constraint calculi allow for rather inexpensive reasoning about entities located in space and time. For this reason, the limited expressiveness of qualitative representation formalism calculi is a benefit if such reasoning tasks need to be integrated in applications. For example, some of these calculi may be implemented for handling spatial GIS queries efficiently and some may be used for navigating, and communicating with, a mobile robot.

Much ink has been spilt on this topic in the artificial intelligence community - on temporal relationships and temporal reasoning. In beginning my review in this area, I will take the Elsevier handbook on Temporal Reasoning as my root node, and begin exploring the research of the PhDs whose work is featured there.

First the three editors

• Michael Fisher
  
• Dov Gabbay
  
• Lluis Vila
  

Next the contributing authors

• Jérôme Euzenat
  
• Angelo Montanari
  
• Peter Jonsson
  
• Manolis Koubarakis
  
• Alfonso E. Gerevini
  
• Mark Reynolds
  
• Clare Dixon
  
• Marc Denecker
  
• Alessandro Artale
  
• Enrico Franconi
  
• Chitta Baral
  
• Michael Gelfond
  
• Jan Chromicki
  
• David Toman
  
• Michael Wooldridge
  
• Maria Fox
  
• Derek Long
  
• Elpida Keravnou-Papailiou
  
• Yuval Shahar
  
• Benjamin Kuipers
  

Side Topics:

Qualitative Spatial Reasoning using Constraint Calculi by Renz and Nebel

Guido Governatori very interesting series of publications on related topics

Approximate Qualitative Temporal Reasoning by Thomas Bittner

Tags: time, temporal reasoning, processes

Artificial Intelligence Applications Institute

Artificial Intelligence Applications Institute Nice looking organization, with some interesting links. Associated with Edinburgh University, so it is reasonable to assume legitimacy of the organization (rather than a group using AI as just a buzzword to generate business).


Tags: artificial intelligence

The Knowledge Representation Requirements Model

Laszlo's Pyramid of Meaning is certainly an interesting way to view the presentation of meaning. It is an upside down triangle, with the narrowest point being "data", which then ascends through "information", "knowledge", and on up through "comprehension", "understanding", "wisdom", and finally "enlightenment". It looks like this (sort of)...

. . . . . enlightenment . . . . . .
. . . . . . wisdom. . . . . . .
. . . understanding . . . .
. . . comprehension . .
. . . knowledge . .
. information .
. . data. .

The objection that I have to the Laszlo pyramid, for my work at least, is that it seems to blur knowledge representation (for automata) at the lower levels, and then knowledge, epistemology, mereology, and a bunch of natural language concepts at the upper levels.

Because of this, I think that my own Knowledge Representation Requirements Model will serve as a better benchmark for what I want to show. It is more in line with the ontological representation method I am proposing, and also in line with the Levels of Conceptual Interoperability Model.

The lowest level of the KRRM is data. Data with semantic identification becomes information. Information in context becomes knowledge. Knowledge in a time-senstive context becomes awareness. Awareness subject to comprehending context changes becomes understanding.

Understanding
Awareness
Knowledge
Information
Data

More later...

Tags: enlightenment, wisdom, understanding, comprehension, knowledge, information, data, knowledge representation

Does Artificial Intelligence worm its way into Studies of Cognition

Artificial Intelligence and Study of Cognition I
Artificial Intelligence and Study of Cognition II

Interesting article (two parts) discussig AI and cognition studies. Often, in the literature, the second is seen as (partially) a rebranding of the first, but I have felt that there is more difference than overlap. Nice to see this author agrees.

Tags: ai, cognition

Comparing

Cognitive Distortion

Interesting to see how these sorts of "leaps in thinking" compare to the techniques of analogical reasoning

Tags: cognition, cognitive distortion, analogic reasoning, artificial intelligence