1. Cover Page
Notational Engineering
and the Search for New
Intellectual Primitives
Author: Jeffrey G. Long (jefflong@aol.com)
Date: September 25, 2002
Forum: Talk presented at the Lawrence Livermore National Laboratory.
Contents
Pages 1‐2: Proposal and Bio
Pages 3‐31: Slides (but no text) for presentation
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2. Title: The Notation is the Limitation: Notational Engineering and the Search for New Intellectual
Primitives
Speaker: Jeffrey G. Long
Date: September 25, 2002
Estimated time: 60 minutes (45 for talk, 15 for Q&A)
The abstractions we use enable our perception, thought and communication, but they can also limit it.
This talk will first present the thesis that in order to understand complex systems, and to adequately
respond to many of the other challenges facing society today, we will need to develop wholly new
abstractions ‐ new intellectual primitives ‐ with which to see and describe nature. It will argue that such
an effort would be greatly accelerated, and made much more likely to succeed, by the creation of a
proposed new discipline called "notational engineering," which will be described.
As an example of notational engineering, the talk will then present a theory of representation which is
based on a new intellectual primitive called a "ruleform". The theory, called "Ultra‐Structure Theory,"
sees entities, structures and relationships as by‐products of complex processes, and postulates that any
process can be represented by a finite but possibly large set of rules. It further hypothesizes that rules
in any format can be converted into an If/Then format, and can be placed into a series of tables based
on the particular "form" of the rules, i.e. how many "If" conditions there are, and how many "Then"
statements there are. These place‐value tables are called "ruleforms", and they offer a practical and
formal, yet highly abstract and concise way of organizing and representing myriad numbers of rules.
Lastly, as an example of a recent application of Ultra‐Structure, the talk will briefly discuss a project that
was done for the Department of Energy to describe the rules of English and the rules of DOE
classification guidance such that a computer could determine the classification level and category of a
text document. The resulting knowledgebase consisted of tens of thousands of rules and was
maintained directly by subject experts (in this case, certified document classifiers).
Further information:
Civilizations have traditionally developed notational systems by accident rather than systematically, so
the hunt for new abstractions could be greatly facilitated by the systematic study of the history and
evolution of a variety of types of notational system, e.g. the branches of mathematics, language and
writing, musical notation, chemical notation, movement and dance notation, and money. In particular
this search would be helped by a good general theory of the structure of notational revolutions such as
occurred with Hindu‐Arabic numerals or the infinitesimal calculus. This proposed new subject of
"notational engineering" would have as a primary goal the development and systematic testing of new
abstractions in many areas, including (e.g.) new ways of representing value besides money, and new
ways of representing complex systems besides the current tools of mathematics, computer science and
natural language.
Ultra‐Structure Theory represents all knowledge of the world in tables of data rather than in the
software of the system, so that the remaining software is "merely" an inference engine that has very
little subject‐specific knowledge. This makes the knowledge (rules) easy to modify and liberates subject
experts to directly manage the knowledge, rather than needing to communicate through a programmer
to change program code.

3. Ultra‐Structure Theory constitutes a merger of expert system and relational database theories which
minimizes the need for software maintenance and maximizes system flexibility. One prediction resulting
from the theory is that all the members of each broad class of systems (e.g. all corporations, all games,
all legal systems, all biological systems) differ from each other in terms of the specific rules governing
their behavior, but not in the form of these rules. In other words, families of systems share the same
"deep structure" or collection of ruleforms.
Biographical Information:
Mr. Long is a Systems Scientist. From 1995‐2002 he worked for DynCorp Systems and Solutions, a
Washington consulting and services firm, on a contract for DOE. Prior to that he worked at The George
Washington University as a Senior Research Scientist, first as director of the Notational Engineering
Laboratory and then also as Deputy Director of the Declassification Productivity Research Center. He
holds a BA degree in Psychology from the University of California at Berkeley.

4. The Notation is
the Limitation
Notational Engineering and the
Search for New Intellectual Primitives
Jeffrey G. Long
September 25, 2002
jefflong@aol.com

5. Proposed outline
P d li
 1: Background on the general problem:
representation and notational systems
 2: Overview of Ultra-Structure: an approach to
complex systems using a new abstraction
 3: Example: The Reviewers Assistance System
September 25, 2002 Copyright 2002 Jeff Long 2

6. 1: The Problem
September 25, 2002 Copyright 2002 Jeff Long 3

7. Many, if not most, of our current problems arise from
y, , p
the way we represent them
 We may have pragmatic competence in using certain kinds
of complex systems but we still don’t really understand
them theoretically
– economics, finance, markets
– medicine, physiology, biology, ecology
 This is not because of the nature of the systems, but rather
because our analytical tools – our notational systems and
the abstractions they reify -- are inadequate
September 25, 2002 Copyright 2002 Jeff Long 4

8. Complexity is not a property of systems; rather,
perplexity is a property of the observer
 Systems appear complex under certain conditions; when
better understood they may still be “complicated” but they
are tractable to explanation
 Using the wrong, or too-limited, an analytical toolset
creates these “complexity barriers”; they cannot be
breached without a new notational system
b h d ih i l
 These problems cannot be solved by working harder,
using faster computers, or moving to OO techniques; they
do not arise due to lack of effort or lack of factual
information
September 25, 2002 Copyright 2002 Jeff Long 5

9. So far we have explored maybe 12 major
abstraction spaces
September 25, 2002 Copyright 2002 Jeff Long 6

10. Notational systems facilitate perception, cognition and
communication
 Each primary notational system maps a different
“abstraction space”
– Abstraction spaces are incommensurable
– Perceiving these is a uniquely human ability
 Acquiring literacy in a notation is learning how to see
a new abstraction space
 Having acquired such literacy, we see the world
differently and can think about it differently
September 25, 2002 Copyright 2002 Jeff Long 7

11. Notational Theory Offers a New Intellectual Synthesis
 Broadened to include all notational systems (not just
language), it sheds light on, and integrates:
l ) i h d li h di
– Whorf’s notion of linguistic relativity,
– Chomsky’s notion of an innate linguistic capability
y g p y
– Toynbee’s notion of the evolution of civilizations by challenge
and response
– parts of numerous other theories in many areas
September 25, 2002 Copyright 2002 Jeff Long 8

12. Conclusions From Section 1
 Every set of intellectual primitives, reified in a
y p ,
notational system, has limitations: these appear to us
in the form of a “complexity barrier”
 Many of the problems we face now as a civilization
a e u da e ta y ep ese tat o a o otat o a
are fundamentally representational or notational
 We need a more systematic way to develop and settle
abstraction spaces: notational engineering
September 25, 2002 Copyright 2002 Jeff Long 9

13. 2: O New Approach
2 One N A h
September 25, 2002 Copyright 2002 Jeff Long 10

14. Current engineering methods work well only under
certain conditions
September 25, 2002 Copyright 2002 Jeff Long 11

15. This is the area addressed by Ultra-Structure Theory
 Ultra-Structure Theory is a general theory of systems
representation, developed/tested starting in 1985
 F
Focuses on optimal computer representation of complex,
i l i f l
conditional and changing rules
 Based on a new abstraction called ruleforms
 The breakthrough was to find the unchanging features of
changing systems
September 25, 2002 Copyright 2002 Jeff Long 12

16. Unfortunately,
Unfortunately Complex and Changing Needs Exist in
Every Organization
Needs
SW & DB
time 1 time 2 time 3...
September 25, 2002 Copyright 2002 Jeff Long 13

17. The theory is based upon a different way of describing
complex systems and processes
observable
behaviors surface structure
generates
rules middle structure
constrains
form of rules
f f l deep structure
September 25, 2002 Copyright 2002 Jeff Long 14

18. As Wolfram has recently argued, rules are a very
y g , y
powerful way of describing things
 Multi-notational: can include all other notational
systems
 Explicitly contingent
 Describe both behavior and mechanism
 H d d of th
Hundreds f thousands can b represented and
d be t d d
executed by a desktop computer
September 25, 2002 Copyright 2002 Jeff Long 15

19. Hypothesis: Any type of assertion can be
reformulated into one or more If-Then rules
 Natural language statements
 Musical scores
 Logical arguments
 Business processes
 Architectural drawings
 Mathematical statements
M th ti l t t t
 But often several “atomic” rules are needed to create
atomic
one “molecular” rule, e.g. “3 strikes and you’re out”
September 25, 2002 Copyright 2002 Jeff Long 16

20. If/Then Rules are Best Represented as Data (records)
Organized into Tables in a Relational Database
O i d i t T bl i R l ti lD t b
If A and B then consider C, D, E, F...
A B C D E F
1
2
Rule #
3
4
5
} 1 Ruleform
n
September 25, 2002 Copyright 2002 Jeff Long 17

21. Structured and Ultra-Structured data are semantically
y
quite different
 Structured data separates algorithms and data, and is
good for data processing and information retrieval
tasks,e.g. reports, queries, data entry
 Ultra-Structured data has only “rules”, formatted in
a manner that allows a very small inference engine
to reason with them using standard deductive logic
 Th inference engine (“animation rules”) software
The i f i (“ i i l ”) f
has little or no knowledge of the external world
September 25, 2002 Copyright 2002 Jeff Long 18

22. The Ruleform Hypothesis
Complex system structures are created by not-
necessarily complex processes; and these
il l d h
processes are created by the animation of
operating rules. Operating rules can be grouped
into a small number of classes whose form is
i ll b f l h f i
prescribed by "ruleforms". While the operating
rules of a system change over time, the ruleforms
remain constant. A well-designed collection of
ruleforms can anticipate all logically possible
operating rules that might apply to the system,
and constitutes the deep structure of the system.
September 25, 2002 Copyright 2002 Jeff Long 19

23. The CoRE Hypothesis
Th C RE H th i
We can create “Competency Rule Engines”, or
CoREs,
C RE consisting of <50 ruleforms, th t are
i ti f 50 l f that
sufficient to represent all rules found among
systems sharing broad family resemblances, e.g.
all corporations. Th i d fi iti d
ll ti Their definitive deep structure
t t
will be permanent, unchanging, and robust for all
members of the family, whose differences in
manifest structures and b h i
if d behaviors will b
ill be
represented entirely as differences in operating
rules. The animation procedures for each engine
will be relatively simple compared to current
applications, requiring less than 100,000 lines of
code in a third generation language.
September 25, 2002 Copyright 2002 Jeff Long 20

24. The deep structure of a system specifies its ontology
 What is common among all systems of type X?
 What is the fundamental nature of type X systems?
 What are the primary processes and entities involved
in type X systems?
 What makes systems of type X different from
systems of type Y?
 If we can answer these questions about a system,
then we have achieved real understanding
September 25, 2002 Copyright 2002 Jeff Long 21

25. Conclusions From Section 2
 One example of a new abstraction is ruleforms To
ruleforms.
truly understand complex systems such as biological
systems, we must get beyond appearances (surface
structure) and rules (middle structure) to the stable
ruleforms (deep structure).
 This is the goal of Ultra-Structure Theory.
September 25, 2002 Copyright 2002 Jeff Long 22

26. 3: Application Example: the
Reviewer’s Assistance System
September 25, 2002 Copyright 2002 Jeff Long 23

27. DOE Reviewer’s Assistance System Requirements
 650 guides defining 65,000 topics that are or may be
classified
 E
Extensive background knowledge required to interpret
i b k dk l d i d i
guidance
 Guidance changes over time
 Terminology in documents changes over time
 The objective is advanced concept spotting, not document
understanding
September 25, 2002 Copyright 2002 Jeff Long 24

28. Normally This Would be Done Using an Expert
System Shell
 ES often have trouble with >1,000 rules; RAS has
>100,000 rules
 K i
Key issue i the maintainability of rules by experts
is h i i bili f l b
 There are many benefits from using relational database to
store rules as data, including:
– Built-in referential integrity
– Easy report-writing and queries
– S bj t experts can maintain knowledgebase directly, without
Subject t i t i k l d b di tl ith t
relying on KE or Programmers
September 25, 2002 Copyright 2002 Jeff Long 25

29. RAS D fi
Defines G id
Guidance Concepts and All P
C t d Possible
ibl
Lexical Expressions of Those Concepts
System Define
Convert Guides Interpretations
Ready
Read Apply Document
Document Guidance Reviewed
September 25, 2002 Copyright 2002 Jeff Long 26

30. Rules Specify Relations Between Topics, Concepts, and
Tokens
T k
September 25, 2002 Copyright 2002 Jeff Long 27

31. Conclusions From Section 3
C l i F S i
 A rule-based system can provide precise and rigorous
interpretation of key DOE terms and concepts
 A rule-based system stored as tables in a relational
database allows creation of a knowledgebase which can
become as large as necessary
 Such a knowledgebase is very easy to specify, change and
review directly by subject experts
September 25, 2002 Copyright 2002 Jeff Long 28

32. References
 Long, J., and Denning, D., “Ultra-Structure: A design theory for
complex systems and processes.” In Communications of the ACM
processes
(January 1995)
 Long, J., “A new notation for representing business and other rules.”
In Long, J. (guest editor), Semiotica Special Issue on Notational
Engineering, Volume 125-1/3 (1999)
 Long, J., “How could the notation be the limitation?” In Long, J.
(guest editor), Semiotica Special Issue on Notational Engineering,
Volume 125-1/3 (1999)
125 1/3
 Long, J., "Automated Identification of Sensitive Information in
Documents Using Ultra-Structure". In Proceedings of the 20th Annual
ASEM Conference, American Society for Engineering Management
(October 1999)
September 25, 2002 Copyright 2002 Jeff Long 29