Notational engineering and the search for new intellectual primitives


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Notational engineering and the search for new intellectual primitives

  1. 1. Cover Page  Notational Engineering and the Search for New Intellectual Primitives   Author: Jeffrey G. Long ( 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   License This work is licensed under the Creative Commons Attribution‐NonCommercial 3.0 Unported License. To view a copy of this license, visit‐nc/3.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.  Uploaded June 27, 2011 
  2. 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. 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. 4. The Notation is the LimitationNotational Engineering and theSearch for New Intellectual Primitives Jeffrey G. Long September 25, 2002
  5. 5. Proposed outlineP 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 SystemSeptember 25, 2002 Copyright 2002 Jeff Long 2
  6. 6. 1: The ProblemSeptember 25, 2002 Copyright 2002 Jeff Long 3
  7. 7. Many, if not most, of our current problems arise from y, , pthe 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 inadequateSeptember 25, 2002 Copyright 2002 Jeff Long 4
  8. 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 informationSeptember 25, 2002 Copyright 2002 Jeff Long 5
  9. 9. So far we have explored maybe 12 major abstraction spacesSeptember 25, 2002 Copyright 2002 Jeff Long 6
  10. 10. Notational systems facilitate perception, cognition andcommunication 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 differentlySeptember 25, 2002 Copyright 2002 Jeff Long 7
  11. 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 areasSeptember 25, 2002 Copyright 2002 Jeff Long 8
  12. 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 engineeringSeptember 25, 2002 Copyright 2002 Jeff Long 9
  13. 13. 2: O New Approach 2 One N A hSeptember 25, 2002 Copyright 2002 Jeff Long 10
  14. 14. Current engineering methods work well only undercertain conditionsSeptember 25, 2002 Copyright 2002 Jeff Long 11
  15. 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 systemsSeptember 25, 2002 Copyright 2002 Jeff Long 12
  16. 16. Unfortunately, Unfortunately Complex and Changing Needs Exist in Every Organization NeedsSW & DBtime 1 time 2 time 3... September 25, 2002 Copyright 2002 Jeff Long 13
  17. 17. The theory is based upon a different way of describing complex systems and processes observable behaviors surface structure generates rules middle structure constrainsform of rulesf f l deep structure September 25, 2002 Copyright 2002 Jeff Long 14
  18. 18. As Wolfram has recently argued, rules are a very y g , ypowerful 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 computerSeptember 25, 2002 Copyright 2002 Jeff Long 15
  19. 19. Hypothesis: Any type of assertion can bereformulated 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. 20. If/Then Rules are Best Represented as Data (records)Organized into Tables in a Relational DatabaseO 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 nSeptember 25, 2002 Copyright 2002 Jeff Long 17
  21. 21. Structured and Ultra-Structured data are semantically yquite 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 worldSeptember 25, 2002 Copyright 2002 Jeff Long 18
  22. 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. 23. The CoRE HypothesisTh 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. 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 understandingSeptember 25, 2002 Copyright 2002 Jeff Long 21
  25. 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. 26. 3: Application Example: the Reviewer’s Assistance SystemSeptember 25, 2002 Copyright 2002 Jeff Long 23
  27. 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 understandingSeptember 25, 2002 Copyright 2002 Jeff Long 24
  28. 28. Normally This Would be Done Using an ExpertSystem 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 ProgrammersSeptember 25, 2002 Copyright 2002 Jeff Long 25
  29. 29. RAS D fi Defines G id Guidance Concepts and All P C t d Possible iblLexical Expressions of Those Concepts System DefineConvert Guides Interpretations Ready Read Apply Document Document Guidance ReviewedSeptember 25, 2002 Copyright 2002 Jeff Long 26
  30. 30. Rules Specify Relations Between Topics, Concepts, andTokensT kSeptember 25, 2002 Copyright 2002 Jeff Long 27
  31. 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 expertsSeptember 25, 2002 Copyright 2002 Jeff Long 28
  32. 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