FUTURE REVOLUTIONS IN CONTENT - project10x.com

2

FUTURE REVOLUTIONSIN CONTENT

Dr. Richard L. BallardChief Scientist

Knowledge Foundations, Inc.

Seybold 365San Francisco, CASeptember 8-12, 2003

3

FUTURE REVOLUTIONS IN CONTENT

1. Knowledge Trumps Programming

2. Semantics Trumps Linguistics

3. Publishing Models Lead Knowledge Capture

4. Enterprises Provide Employee Knowledge

5. Semantic Web Paths Constrain Decision Trade-offs

6. More Milestones on the Road Ahead

Seybold 365 Conference Bullets.dsf Copyright Richard L. Ballard 2003

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Knowledge Trumps Programming

Seybold 365 Knowledge Trumps.dsf Copyright Richard L. Ballard 2003

Learning Lessons fromLearning Lessons fromLearning Lessons fromLearning Lessons fromAnimal EvolutionAnimal EvolutionAnimal EvolutionAnimal Evolution

5

BRAINSBRAINSBRAINSBRAINS ProduceProduceProduceProduceMemory,Memory,Memory,Memory,

Learning,Learning,Learning,Learning,Experience,Experience,Experience,Experience,

THEORYTHEORYTHEORYTHEORY &&&&TeachingTeachingTeachingTeaching

INFORMATIONINFORMATIONINFORMATIONINFORMATIONProducesProducesProducesProducesSituationSituationSituationSituation

AwarenessAwarenessAwarenessAwareness& & & &

InstinctiveInstinctiveInstinctiveInstinctiveResponseResponseResponseResponse

1 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010

Genetic Knowledge Storage (DNA Bits)

1

101

102

103

104

105

106

107

108

109

1010

1011

1012

1013

1014

Brain Knowledge Storage (N

eural Bits)

Adapted from The Dragon's of Eden, Carl Sagan, 1977

Humans

Mammals

Reptiles

Jellyfish

Virus

ProtozoaAlgaeBacteria

Amphibians

ImplyingImplyingImplyingImplyingKnowledge TypeKnowledge TypeKnowledge TypeKnowledge TypeFromFromFromFromStorage TypeStorage TypeStorage TypeStorage Type

Genes vs Brains

Th

eo

re

tica

l Kn

ow

led

ge

Th

eo

re

tica

l Kn

ow

led

ge

Th

eo

re

tica

l Kn

ow

led

ge

Th

eo

re

tica

l Kn

ow

led

ge

Instinctual KnowledgeInstinctual KnowledgeInstinctual KnowledgeInstinctual Knowledge

UnderstandingUnderstandingUnderstandingUnderstandingKnowledgeKnowledgeKnowledgeKnowledge

Seybold Biological Knowledge Types.dsf Copyright Richard L. Ballard 1998-2003

LEARN - new theoryfrom experience

Behaviors change to model success

TEACH - theory inextended childhood

AdaptiveAdaptiveAdaptiveAdaptiveKnowledge-BasedKnowledge-BasedKnowledge-BasedKnowledge-Based

12

InstinctiveInstinctiveInstinctiveInstinctiveInformation-BasedInformation-BasedInformation-BasedInformation-Based

NEVER LEARN - simply compete,

best suited survives

INSTINCT - DNA programed responsesto sensory awareness

6

1

10 1

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 9

10 10

10 11

10 12

10 13

10 14

10 15

10 16

10 17

10 18

10 19

10 20

10 21

101 10181 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 10151016 1017

Human

Mammals

AmphibiansJellyfish

ProtozoaAlgae

Virus

ModernModernModernModernCivilizationCivilizationCivilizationCivilization

Workgroups

SurvivalSurvivalSurvivalSurvivalin in in in SpaceSpaceSpaceSpace

Bacteria

Reptiles

Turn TowardTurn TowardTurn TowardTurn TowardAll Theories --All Theories --All Theories --All Theories --

All KnowledgeAll KnowledgeAll KnowledgeAll Knowledge

LogicLogicLogicLogic++++

InformationInformationInformationInformationSOFTWARE

HARDWARE

DOS Windows 98

486 PC

PentiumLaptop

386 PC

KNOWLEDGE AGEKNOWLEDGE AGEKNOWLEDGE AGEKNOWLEDGE AGEREQUIREMENTSREQUIREMENTSREQUIREMENTSREQUIREMENTS

Turning FromInformationToward theCoding of

Theory

Knowledge = Information + TheoryKnowledge = Information + TheoryKnowledge = Information + TheoryKnowledge = Information + Theory

Information AgeSoftware Has

Fixed Behaviors &Must Be Replaced.It Has No Capacityto Learn & Change.

Seybold Civilzation & Computing.dsf Copyright Richard L. Ballard 2003

Single CodingSingle CodingSingle CodingSingle CodingSystemSystemSystemSystem

forforforforALLALLALLALL

KNOWLEDGEKNOWLEDGEKNOWLEDGEKNOWLEDGE

7

++++

est.85%

est.15%

What You Can LearnOnce & Know Forever

Answers to:

WHY?HOW?

WHAT IF?

Costs:Invest Once

Employ Forever

Dies, if left onlyin Human Heads

Loss:

TheoryTheoryTheoryTheoryA' PrioriA' PrioriA' PrioriA' Priori

Up-to-the-MomentSituation Awareness

Answers to:WHO?

WHERE?WHEN?WHAT?

HOW MUCH?

Costs:Pay A LittleEvery Day

Old NewsTomorrow

Loss:

InformationInformationInformationInformationA' PosterioriA' PosterioriA' PosterioriA' Posteriori

Economics of Knowledge FormEconomics of Knowledge FormEconomics of Knowledge FormEconomics of Knowledge Form

Seybold Economics of Knowledge Form.dsf Copyright Richard L. Ballard 2002

8

0000 10101010 20202020 30303030

De

plo

ym

en

tD

ep

loy

me

nt

De

plo

ym

en

tD

ep

loy

me

nt

Basic &Basic &Basic &Basic &ExploratoryExploratoryExploratoryExploratory

ResearchResearchResearchResearch

PreliminaryPreliminaryPreliminaryPreliminaryto Advancedto Advancedto Advancedto Advanced

DevelopmentDevelopmentDevelopmentDevelopment

Productization,Productization,Productization,Productization,Industry Industry Industry Industry

FormationFormationFormationFormation& Emergence& Emergence& Emergence& Emergence

20 years Back20 years Back20 years Back20 years Back 20 years Foreward20 years Foreward20 years Foreward20 years Foreward

KNOWLEDGE TECHNOLOGY

KnowledgeKnowledgeKnowledgeKnowledgeTechnologyTechnologyTechnologyTechnology

TodayTodayTodayToday

Physical Theory ofPhysical Theory ofPhysical Theory ofPhysical Theory ofKnowledge &Knowledge &Knowledge &Knowledge &

ComputingComputingComputingComputing1993199319931993

Key Research and Development Sponsors

Cost of Cost of Cost of Cost of KnowledgeKnowledgeKnowledgeKnowledge

crashcrashcrashcrash1984198419841984

Creating A New Science and TechnologyCreating A New Science and TechnologyCreating A New Science and TechnologyCreating A New Science and TechnologyTo Bridge fromTo Bridge fromTo Bridge fromTo Bridge from Information AgeInformation AgeInformation AgeInformation Age totototo Knowledge AgeKnowledge AgeKnowledge AgeKnowledge Age

Seybold Technology Transition.dsf Copyright Richard L. Ballard 2003

Fiction Game EnginesFiction Game EnginesFiction Game EnginesFiction Game Engines& & & & "Star Wars""Star Wars""Star Wars""Star Wars"

Battle ManagementBattle ManagementBattle ManagementBattle Management

Space & DefenseSpace & DefenseSpace & DefenseSpace & DefensePrograms ManagementPrograms ManagementPrograms ManagementPrograms Managementof National Importanceof National Importanceof National Importanceof National Importance

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Seybold Essential Knowledge Technology.dsf Copyright Richard L. Ballard 2003

KNOWLEDGE TECHNOLOGY -- Essential Characteristics

Unchanging Programmed Base becomes Fixed Hardware BaseUnchanging Programmed Base becomes Fixed Hardware BaseUnchanging Programmed Base becomes Fixed Hardware BaseUnchanging Programmed Base becomes Fixed Hardware Base

Current Record: 13+ years, no recompile Windows 2 to Windows XPExpectation: Disappearance of CPU and logic centric processing

Very Slow Evolving, Subject Independent, All Knowledge CodesVery Slow Evolving, Subject Independent, All Knowledge CodesVery Slow Evolving, Subject Independent, All Knowledge CodesVery Slow Evolving, Subject Independent, All Knowledge Codes

Current Record: 20+ years, Mx Semantic Concept CodesExpectation: Early Evolution focused in the Linguistic - Semantic Gap

Professions, Publishers, & Government Lead Ontology Codification Professions, Publishers, & Government Lead Ontology Codification Professions, Publishers, & Government Lead Ontology Codification Professions, Publishers, & Government Lead Ontology Codification

Current Record: Minimum asset ontology 5K-50K concepts, schema are dyingExpectation: Ontologies will top 50-500 million concepts in next 10 years

Theory Is Not Stored in Linguistic, 2 Dimensional FormsTheory Is Not Stored in Linguistic, 2 Dimensional FormsTheory Is Not Stored in Linguistic, 2 Dimensional FormsTheory Is Not Stored in Linguistic, 2 Dimensional Forms

Current Record: Minimum asset relations 250-500, simulations 2K-4K Expectation: DVD Semantic webs might appear in books starting 2005

10Seybold Knowledge Trumps Summary.dsf Copyright Richard L. Ballard 2003

SUMMARY

Knowledge Content is a Capital AssetKnowledge Content is a Capital AssetKnowledge Content is a Capital AssetKnowledge Content is a Capital Asset

Knowledge Ownership and PracticeKnowledge Ownership and PracticeKnowledge Ownership and PracticeKnowledge Ownership and PracticeWill Define an Organization's ValueWill Define an Organization's ValueWill Define an Organization's ValueWill Define an Organization's Value

Knowledge Permanence & 30-100 year Change Cycles Knowledge Permanence & 30-100 year Change Cycles Knowledge Permanence & 30-100 year Change Cycles Knowledge Permanence & 30-100 year Change Cycles Frame Revolutionary New Vistas for World PlanningFrame Revolutionary New Vistas for World PlanningFrame Revolutionary New Vistas for World PlanningFrame Revolutionary New Vistas for World Planning

Machine Theories and Computation willMachine Theories and Computation willMachine Theories and Computation willMachine Theories and Computation willModel Human Theories and ReasoningModel Human Theories and ReasoningModel Human Theories and ReasoningModel Human Theories and Reasoning

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Seybold Semantics Trumps.dsf Copyright Richard L. Ballard 2003

Semantics Trumps Linguistics

Representation asRepresentation asRepresentation asRepresentation asa Precise Sciencea Precise Sciencea Precise Sciencea Precise Science

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Philosophy -- Language for "All Knowledge"Philosophy -- Language for "All Knowledge"Philosophy -- Language for "All Knowledge"Philosophy -- Language for "All Knowledge"

Copyright Richard L. Ballard 1994-2003

ENTITYRELATIONSHIP

MEDIATINGSTRUCTURE(taxonomy,aggregations,network.flow. QFD,..)

n=2 n>2

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3 Metaphysics

TheologyCosmology

ChemistryBiology

SociobiologyPsychobiology

Energy

Matter

FormsEmpiricismRationalism

Sense Data

Phenomena

Epistemology

Concepts

MaterialsMachines

LANGUAGE

ARTCategories

Models Exemplars

Realistic

Beauty

Truth

Pure Intellect

FormalLanguages

NaturalLanguages

Mathematics

Logic

Measurement

Icons Photographs

SYMBOLS& CODES

Ontology

The "a priori" rational constraintsof belief and accepted theory

Mental Concepts & Methodology Observed RealityThe "a posteriori" constraints of

observed fact, material existence,and recorded measurement

PersistentRepresentations

of Knowledge

Measured by InformationMeasured by Theory

Physics

PhysicalPhysicalPhysicalPhysicalUniverseUniverseUniverseUniverse

Abstract

UniversalsUniversalsUniversalsUniversals ParticularsParticularsParticularsParticulars

AbsoluteAbsoluteAbsoluteAbsoluteBeingBeingBeingBeing

----PerfectPerfectPerfectPerfect

IntelligenceIntelligenceIntelligenceIntelligence PlanetsAnimals

Semantic Modeling as the Means of Knowledge CaptureSemantic Modeling as the Means of Knowledge CaptureSemantic Modeling as the Means of Knowledge CaptureSemantic Modeling as the Means of Knowledge Capture

"Semantic Networks""Semantic Networks""Semantic Networks""Semantic Networks"

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Model Model Model Model - - - - InstanceInstanceInstanceInstance Concept Codes Concept Codes Concept Codes Concept Codes


of Knowledge

PhysicsMetaphysics

ModelPhysicalInstances

1 - NInstance 0

Meta-Physical

Instances1 - N

Model

Instance 0




IntelligenceIntelligenceIntelligenceIntelligence

Models are metaphysicaland typically have

plural concept titles

Seybold Model Instance Codes.dsf Copyright Richard L. Ballard 2003

Conceptualism & SemanticsConceptualism & SemanticsConceptualism & SemanticsConceptualism & SemanticsReplace LanguageReplace LanguageReplace LanguageReplace Language

Same for All LanguagesSame for All LanguagesSame for All LanguagesSame for All Languages

MEANING is defined only by the uniqueweb of relationships tying every conceptto those connecting to it.

A CONCEPT (model-instance) appearsonly once in any semantic web, the codes locate it instantly without search

SEARCH is an artifact ofoverloaded symbol use.

In coded, declarative,semantic webs there

is no search of any kind.

Properly implemented, Properly implemented, Properly implemented, Properly implemented, SEMANTIC WEBS approachSEMANTIC WEBS approachSEMANTIC WEBS approachSEMANTIC WEBS approachthe absolute limitsthe absolute limitsthe absolute limitsthe absolute limits on size, speed, and efficiency.on size, speed, and efficiency.on size, speed, and efficiency.on size, speed, and efficiency.

The Reality Web &Semantic Web are

fundamentally different,but will interact constantly.

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Adapting Semantics toAdapting Semantics toAdapting Semantics toAdapting Semantics toAll Theories RepresentationAll Theories RepresentationAll Theories RepresentationAll Theories Representation

Ballard/Sowa/Peirce SynthesisBallard/Sowa/Peirce SynthesisBallard/Sowa/Peirce SynthesisBallard/Sowa/Peirce Synthesis

Semantic RepresentationIndependent

ALLKNOWLEDGE

Copyright Richard L. Ballard 2002Copyright Richard L. Ballard 2002Copyright Richard L. Ballard 2002Copyright Richard L. Ballard 2002

L e ve l 1

L e ve l 2

L e ve l 3

Patterns of OrganizationPatterns of OrganizationPatterns of OrganizationPatterns of Organization

Concept Taxonomy

Relationship Taxonomy Mediating Structure

Taxonomy

RelationalRelationalRelationalRelationalConstraintsConstraintsConstraintsConstraints

SubjectsSubjectsSubjectsSubjectsInvolvedInvolvedInvolvedInvolved

Relative

Mediating

Contributor: John F. Sowa

Physical Physical Physical Physical

Metaphysical Metaphysical Metaphysical Metaphysical

TimeTimeTimeTimeDep.Dep.Dep.Dep.

TimeTimeTimeTimeIndep.Indep.Indep.Indep.

Law

Theory

Observe

Faith

12 or 1812 or 1812 or 1812 or 18Primitive ConceptPrimitive ConceptPrimitive ConceptPrimitive Concept

TypesTypesTypesTypes

Philosophical Standard




15Seybold Semantics Trumps Summary.dsf Copyright Richard L. Ballard 2003

SUMMARY

Centered on Semantic Form that ScienceCentered on Semantic Form that ScienceCentered on Semantic Form that ScienceCentered on Semantic Form that ScienceSolves Seemingly Unsolvable Complexity ProblemsSolves Seemingly Unsolvable Complexity ProblemsSolves Seemingly Unsolvable Complexity ProblemsSolves Seemingly Unsolvable Complexity Problems

Pure Declarative, Semantic Webs Have BeenPure Declarative, Semantic Webs Have BeenPure Declarative, Semantic Webs Have BeenPure Declarative, Semantic Webs Have BeenTested for 12+ years in Significant ApplicationsTested for 12+ years in Significant ApplicationsTested for 12+ years in Significant ApplicationsTested for 12+ years in Significant Applications

First Projects Should Begin Emerging CommerciallyFirst Projects Should Begin Emerging CommerciallyFirst Projects Should Begin Emerging CommerciallyFirst Projects Should Begin Emerging Commerciallyin 2004-6 as Desktop Product Discs and Downloadsin 2004-6 as Desktop Product Discs and Downloadsin 2004-6 as Desktop Product Discs and Downloadsin 2004-6 as Desktop Product Discs and Downloads

Theories of Knowledge and ComputationTheories of Knowledge and ComputationTheories of Knowledge and ComputationTheories of Knowledge and ComputationDefine an Emerging Science of Representation Define an Emerging Science of Representation Define an Emerging Science of Representation Define an Emerging Science of Representation

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Seybold Publishing Leads.dsf Copyright Richard L. Ballard 2003

Publishing Models Lead Knowledge Capture

Amassing & Organizing Amassing & Organizing Amassing & Organizing Amassing & Organizing A DominatingA DominatingA DominatingA Dominating

Reference SourceReference SourceReference SourceReference Source

Markets, Sources, & ProductionMarkets, Sources, & ProductionMarkets, Sources, & ProductionMarkets, Sources, & Production

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T-38NOPERATIONS &MAINTENANCE

Ellington Field,Houston, TX

EllingtonOrganization Aircraft Systems FltStatus Discrepancies Tasks Reports BACK HELP

T-38AT-38NKC-135RWB-57FTail Number

AvionicsTacanJEDS

CalendarFlightsCheck FlightsAircraft Status

IndicationsDiagnosisRecommend

InspectionsCorrectionsCheck-out

NASAJSCEllington FldManufacturersPilots/ChiefsDoD

Pilots CrewChiefs

SPACE SHUTTLE SUPPORT OFFICE

NA SA OR GAN IZATIO N

AdministratorNASA Headquarters

D irector -Am es R esearch

C enter

D irector - George M arshal l

Space F light C enter

D irector - Lyndon J ohns on

Space C enter

D irector -John KennedySpac e C enter

Office of the Di rec torF light C rew Operations

C hief -Space StationSupport Offic e

C hief -Space ShuttleSupport Offic e

C hief -As tronaut

Office

Office of the Director - JSC

Director -Administration

Director - Flight CrewOperations

Director -Mission Operations

MAGN AVOX

MOTOROLA

R O CK W E LL IN T ER N A TIO N A L

NORTHRUP GRUMMAN

Northrop Grumman Corporation

Board ofDirectors

ExecutiveOffices

CorporateStaff

ElectronicSystems Division

Aircraft Division - Northrop

B-2 Division - Northrop

Northrop World WideAircraft Services

T-38 / F-5 ProgramOffice

D E P A R T M E N T O F D E F E N S E

Secretary of D efense

T-38Modifications

Manager

T-38SystemsManager

T-38AvionicsManager

Tactical Trainer SystemsProgram Management

3246Tactical

Wing

6510Tactical

Wing

CommanderSan Antonio

ALC

AircraftManagementDirectorate

San AntonioAir Logistical Center

Chief Scientistof the Air Force

USAF MaterielCommand

Air TrainingCommand

Air CombatCommand

Chief of Staffof the Air Force

Office of theSecretaryof Defense

Secretary ofthe Air Force

Secretary ofthe Army

Secretary ofthe Navy

A T - 3 8

L o t 1

T - 3 8 N

F - 5T - 3 8D e r iv a tiv e s

V a r ian ts M o d el S e q u en c e

T - 3 8 A

L o t S e q u en c e

L o t 2 .. .

T -3 8 A IR C R A F T D E S IG N

T A C A N S Y S T E M /S U B S Y S T E M

T -3 8 NT A C A N

T ra n s c e i v e r S y s te m

A R N - 1 5 4N A V R e c e i v e r

( T A C A N )

T - 3 8 NR F

L in eS w i t c h

T - 3 8 NU p p e r

L B a n dA n t e n n a

T - 3 8 NL o w e r

L B a n dA n t e n n a

TACAN WIRING DIAGRAM

LowerL Band

Antenna

UpperL Band

Antenna

SymbolGenerator

No 1

SymbolGenerator

No 2

KDA 1Data

Adapter

KDA 2Data

Adapter

NavigationManagement

Unit

AudioControlPanel -Front

T-38NAvionics Bay

DC Circuit BreakerPanel

T-38N28 Volt

DC Power

AudioControlPanel -Rear

Serial Data FlowRF Signal Flow

T-38NRF TRANSLine Switch

Power Flow

Control Flow

A R N - 1 5 4N A V R e c e iv e r

( T A C A N )

T-38N FUNCTIONAL DECOMPOSITION

T-38NT-38

AirframeT-38N

PropulsionT-38N

AvionicsT-38N

Loadouts

T-38NSingle

FunctionControl

T-38NMultiFunction

Display

T-38NMultifunction

Control

T-38NRadar

T-38NRadio

T-38NSelf

Contained

T-38NWeather

Radar Display

T-38NRadio

Management

ARN-147(V)Nav Receiver

VOR/LOC/GS/MB

ARN-154NAV Receiver

(TACAN)

T-38NData

Handl ing

T-38NContro ls

& Disp laysT-38N

Identi ficationT-38N

M oni tor ing &R ec ord ing

T -38NN avigation

T-38NRecon

T-38NTarget /

Acquisition& Strik e

DiscrepancyReport ID

T ai l Num berDate

D AILY R EPO R T

S e t o fR e co m m e nd e d

R e m e d ie s

Set ofFault Diagnosis

Set ofDiscrepancy

Reports


YEAR

MonthsDays

CALENDAR

D isc re p an cyR e po r t ID

P il ot N a m e

Da te

F ILED BYREPORTED FIX


Corrective Action

Crew Chief

Flight Status

Tail Number

Date

FLIGHT STATUS

Flight S tatus

Fly -No

Problem

Fly -Some

DiscrepanciesGrounded

AIRCRAFT STATUS

REPORTED FAULTFault Indication

Display


Fault D iagnosis

F ault Ind ication

Display

EQUIP DIAGNOTICS

RECOMM ENDED FIX

RecommendedAction

Corrective Action

Crew ChiefS e t o f

C o rre ctiv e A c tio n s

Set ofPart Changes

TACAN P REFLIGHT TES T P ROCEDURE

TACAN INSTALLATION PROCEDURE

ARN-154 TransceiverRemoval Procedure

Gain accessto right handavionics bay

On Tacan,disconnectI/O multi-pin

cableOn Tacan,disconnect

coax antenna cable

Cut safetywire from

retaining bolt

. . .

TACAN REM OVAL PROCEDURE

REPAIR PROCEDURE

Work BreakDown

Part Number

Wiring/Fault Diagram

PART INSTALLED

Corrective Action

Tail Number

Part Number

PART REMOVED

Corrective Action

Tail Number

Part Number

NASAOrganization

PartsManufacturers

AircraftProgram

Managers

AircraftOrganizations/Personnel

AircraftSystem/Subsystem/Parts

Tail # 900-944

Wiring DiagramFault Tree

Work BreakdownStructures

Maintenance &Repair History

Work Orders

Semantic Knowledge LayersSemantic Knowledge LayersSemantic Knowledge LayersSemantic Knowledge Layers

By By By By PROFESSIONPROFESSIONPROFESSIONPROFESSION or or or or INDUSTRY INDUSTRY INDUSTRY INDUSTRY

Complete WorkingComplete WorkingComplete WorkingComplete WorkingKnowledgeKnowledgeKnowledgeKnowledge

Seybold Working Knowledge.dsf Copyright Richard L. Ballard 2003

Theory Reuse> 90%

SubscriptionInformation

LayerDownloads

18Seybold Value Experience.dsf Copyright Richard L. Ballard 2003

Asset Value ExperienceAsset Value ExperienceAsset Value ExperienceAsset Value ExperienceCompetitive Knowledge SuperiorityCompetitive Knowledge SuperiorityCompetitive Knowledge SuperiorityCompetitive Knowledge Superiority

Overwhelming speed, knowledge, and predictive success at forward points of contact, sales, support, and supervision

Productive Labor Category TransitionsProductive Labor Category TransitionsProductive Labor Category TransitionsProductive Labor Category Transitions

Knowledge asset use to gain productivity by transitioning from professional to para-professional labor categories

Adaptive Learning & Experience GainAdaptive Learning & Experience GainAdaptive Learning & Experience GainAdaptive Learning & Experience Gain

System awareness of failures and capacities to reason, learn, and invent more successful theories and behavior

Knowledge Creation & Assured OwnershipKnowledge Creation & Assured OwnershipKnowledge Creation & Assured OwnershipKnowledge Creation & Assured Ownership

Organizational self-knowledge of assets and predictive success at creating new assets and opportunities

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Project Manager / Product Designer

Rational Modelers

Outliners / Transformers / Production

Knowledge Editors

Subject Specialists

Responsible to customer requirements, schedule, budget, development plan, product design, and project goals.

Identifies essential questions, issues, and options.Models from authoritive sources, the flow of theory, rationale, evidence, and consequent outcome.Directs source document transformations and knowledge capture.

Automates & reviews the transformation of soft sources (books, databases, reports, forms, etc.) into hard coded formats suited to integration, editing, and validation.Identifies and details any exceptions to modeling assumptions.

Enforces product design, artistic, granularity, abstraction, and best practice constraints.Directs content integration, language self- consistency, and subject specialist review.Tests accuracy, completeness, and form with automated tools.

Review and validate edited content for consistency to subject norms.Propose new topics and finer-grain distinctions to reflect user needs.Manage and report user tests.

Full Time Commitments

Knowledge Asset Modeling TeamsKnowledge Asset Modeling TeamsKnowledge Asset Modeling TeamsKnowledge Asset Modeling Teams

Seybold Modeling Teams.dsf Copyright Richard L. Ballard 2003

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Word Processor

"Outliner"

"Modeler"

1

2

3

4

5

Text Books

Documents

Data Bases

TheologyCosmology

ChemistryBiology


Energy

Matter


Sense Data

Phenomena

Epistemology

Concepts

MaterialsMachines

LANGUAGE

ARTCategories

Models Exemplars

Realistic

Beauty

Truth

Pure Intellect

FormalLanguages

NaturalLanguages

Mathematics

Logic

Measurement

Icons Photographs

SYMBOLS& CODES

Ontology


of Knowledge

Measured by InformationMeasured by Structure


Abstract





Categories

ModelsAbstractionHierarchy

AvionicsFunctional Areas

Controls & Displays

Flight Control

Communications

Navigation

Identification

Monitoring & Recording

Data Handling

Reconnaissance

Target Acquistion & Strike

Electronic Combat

Auto Direction FinderDirection MeasuringFlight Track NavigationGlobal PositioningMicrowave LandingNavigation ReceiverRadio AltimeterRadio BeaconRadio CompassRadio ReceiverRadio TransmitterStation KeepingTACANTraffic Alert/Collision Avoidance

Radio Navigation

Radar Navigation

Self-Contained Navigation

GPSGSILSLOCLORANMBOMEGASHORANTACANVOR

LFHFVHFUHF

51R51V51ZAPN-70APN-151APN-157ARN-12ARN-14ARN-18ARN-32ARN-58ARN-67ARN-78ARN-82ARN-84ARN-92ARN-108ARN-109ARN-120ARN-123ARN-127ARN-131ARN-136ARN-139ARN-147CMA-764EDO-1200GNS-500ILS-70KLN-670KNS-660KTU-709LORAN CLTN-211LTN-2001MKA-28TCN-500TDL-800UNS-1VIR-30VIR-31VIR-34VN-411VNS-41VOR-101

AvionicsFunction

System Attribute Values

System Decomposition

SystemTest Procedures

Set of Required Systems

System Function Classification

System Data Flow Diagrams

RadioNavigation

SystemModel

All Models All Sources

1213

14

Full & Part-Time"Editors"

Sponsor'sConsulting

Content"Experts"9

7

8

10

6

11

16

21

22

18

23 UserGuide

19

24

20

17

15

SOURCE AQUISITION & MODELINGEDITING &

MODELVALIDATION

INTEGRATION& ONTOLOGY

RATIONALE TEST &PRODUCTIZATION

BUILDER

PRODUCTION PROCESSPRODUCTION PROCESSPRODUCTION PROCESSPRODUCTION PROCESS

Copyright Richard L. Ballard 2003Copyright Richard L. Ballard 2003Copyright Richard L. Ballard 2003Copyright Richard L. Ballard 2003Seybold Production Process.dsfSeybold Production Process.dsfSeybold Production Process.dsfSeybold Production Process.dsf

21

Seybold Mapping Document Structures.dsf Copyright Richard L. Ballard 2002-3

Modeling Significant Document StructuresModeling Significant Document StructuresModeling Significant Document StructuresModeling Significant Document Structures

BudgetCategories

6.2 6.3

DTAP &DTO Funding

1997TechnologyArea Plans

10 plans

2 -14 / Area (5 median)

TechnologySub-Area

Plans

Defense TechnologyArea Plan (DTAP)

April 1996KRC Doc # 36

Sub-AreaDevelopment

Efforts

ProgramElements

PerformanceAttribute Sets

IndustryPrograms

GovernmentPrograms

ProgrammedPE Effort

PlatformProductCapability

DeficiencyScenario

AssumedBaselines

Technology BaseMetrics of

Performance (MOP)

ChallengeStatements

1995200020052010

Time Periods

TechnologyAreas

10 Areas

Tech Sub-Area

Time Period

Baseline

Transition Goals

Tech Area

Budget

DTO SupportArea Total

FY 1997

Baseline

Pay-off Objective

ImprovementSought (%)

Attribute Set

Adv TechDemos

(ATD/ACTD)

DefenseTechnologyObjectives

Sub-Area Roadmap

Tech Sub-Area

Fiscal Year

Develop. Effort

Performance Performance Performance Performance TheoryTheoryTheoryTheory

N=5N=5N=5N=5

NationalObjectives

SecurityObjectives

MilitaryObjectives

CampaignObjectives

OperationalObjectives

OperationalTasks

KeyAttribute

Requirements

STRATEGYTO TASK

qfdqfdqfdqfd

Source Acquisition & Rational ForensicsSource Acquisition & Rational ForensicsSource Acquisition & Rational ForensicsSource Acquisition & Rational Forensics

PolicyPolicyPolicyPolicyTrade-OffTrade-OffTrade-OffTrade-Off

TheoryTheoryTheoryTheoryN=14N=14N=14N=14

22

THEORYTHEORYTHEORYTHEORY as N-ary Bundleas N-ary Bundleas N-ary Bundleas N-ary Bundle

Aircraft LotAircraft Model / Block

Lot1 Lot 2 Lot 3 Lot 4 Lot 5 Lot 6 Lot 7

C-130A

Max Aircraft / MonthProduction Rate

AircraftTotal CostAttributes

Airframe Cost

Propulsion Cost

Avionics Cost

EntityEntityEntityEntityN=1N=1N=1N=1

BinaryBinaryBinaryBinaryRelationRelationRelationRelation

N=2N=2N=2N=2

TertiaryTertiaryTertiaryTertiaryRelationRelationRelationRelation

N=3N=3N=3N=3

N-aryN-aryN-aryN-aryRelationRelationRelationRelationN=2,3,4,N=2,3,4,N=2,3,4,N=2,3,4,

5, 6, 7,......5, 6, 7,......5, 6, 7,......5, 6, 7,......

Angle of ClimbAltitude

Air SpeedRoll Rate

Test Aircraft

ROLL MEASUREMENT

N5 10 15

~N2

~N

ExplosiveExplosiveExplosiveExplosiveComplexity GrowthComplexity GrowthComplexity GrowthComplexity Growth

Costs Are Linear In

Information Content

Semantics ControlsSemantics ControlsSemantics ControlsSemantics ControlsComplexity GrowthComplexity GrowthComplexity GrowthComplexity Growth

Going fromGoing fromGoing fromGoing fromInformationInformationInformationInformation to to to to TheoryTheoryTheoryTheory

ImpossibleImpossibleImpossibleImpossibleExecution &Execution &Execution &Execution &

Database CostsDatabase CostsDatabase CostsDatabase Costs

N -- InformationalN -- InformationalN -- InformationalN -- InformationalConditionsConditionsConditionsConditions

Outcome Predicted Outcome Predicted Outcome Predicted Outcome Predicted by Theoryby Theoryby Theoryby Theory


DeficiencyScenario

AssumedBaselines Tech Sub-Area

Time Period

Baseline

Transition Goals

DTO Support

Area Total

Baseline

Pay-off Objective


Attribute Set


N=5N=5N=5N=5

CONTENTCONTENTCONTENTCONTENT -- JUST ANOTHER THEORY ..... -- JUST ANOTHER THEORY ..... -- JUST ANOTHER THEORY ..... -- JUST ANOTHER THEORY .....

Publishers Take the Lead with SemanticsPublishers Take the Lead with SemanticsPublishers Take the Lead with SemanticsPublishers Take the Lead with Semantics

Copyright Richard L. Ballard 2003Seybold Semantic Master Complexity.dsf

23Seybold Publishers Lead Summary.dsf Copyright Richard L. Ballard 2003

SUMMARY

The Marshalling and Authentication of Any Body ofThe Marshalling and Authentication of Any Body ofThe Marshalling and Authentication of Any Body ofThe Marshalling and Authentication of Any Body ofTheory is a Capital Expense Conveying Ownership &Theory is a Capital Expense Conveying Ownership &Theory is a Capital Expense Conveying Ownership &Theory is a Capital Expense Conveying Ownership &

Creating Substantial Barriers to CompetitionCreating Substantial Barriers to CompetitionCreating Substantial Barriers to CompetitionCreating Substantial Barriers to Competition

Dominating Theory Positions in EconomicallyDominating Theory Positions in EconomicallyDominating Theory Positions in EconomicallyDominating Theory Positions in EconomicallySignificant Markets Create the FrameworksSignificant Markets Create the FrameworksSignificant Markets Create the FrameworksSignificant Markets Create the Frameworksfor Structuring All Tasks & Information Use for Structuring All Tasks & Information Use for Structuring All Tasks & Information Use for Structuring All Tasks & Information Use

Theories Remain Relevant for 1000s of Years,Theories Remain Relevant for 1000s of Years,Theories Remain Relevant for 1000s of Years,Theories Remain Relevant for 1000s of Years,Independent of Facts & Language ChangesIndependent of Facts & Language ChangesIndependent of Facts & Language ChangesIndependent of Facts & Language Changes

that Make Them Appear Differentthat Make Them Appear Differentthat Make Them Appear Differentthat Make Them Appear Different

Publishers Already Have the Business and StaffPublishers Already Have the Business and StaffPublishers Already Have the Business and StaffPublishers Already Have the Business and StaffModels to Transform Existing Assets into ProductsModels to Transform Existing Assets into ProductsModels to Transform Existing Assets into ProductsModels to Transform Existing Assets into Products

24

Seybold Enterprises Provide.dsf Copyright Richard L. Ballard 2003

Enterprises Provide Employee Knowledge

Who Will Own TheWho Will Own TheWho Will Own TheWho Will Own TheCompany's Knowledge?Company's Knowledge?Company's Knowledge?Company's Knowledge?

25

Mastering Situation with IntentMastering Situation with IntentMastering Situation with IntentMastering Situation with Intent

Management:

Making things happenthat would never happen

by themselves

Seybold Mastering Situation.dsf Copyright Richard L. Ballard 2003

26

EmployeeEmployeeEmployeeEmployeeManagerManagerManagerManager

Theory ConstraintsTheory ConstraintsTheory ConstraintsTheory Constraints Reality ConstraintsReality ConstraintsReality ConstraintsReality Constraints

Education & ExperienceJob Requirements

Concepts of OperationPlans & Task Theories

Management ToolsResource Control

Situational InformationTask InstructionsWorkload & ProgressDiscretionary ResourcesAssigned ResourcesNon-work Commitments

EXPRESSEDORGANIZATIONAL

INTENT

REACTION &FEEDBACK

Different TransmittedDifferent TransmittedDifferent TransmittedDifferent TransmittedOrders & IntentOrders & IntentOrders & IntentOrders & Intent

Different ReactionsDifferent ReactionsDifferent ReactionsDifferent Reactions& Feedback& Feedback& Feedback& Feedback

New Options and ResourcesNew Options and ResourcesNew Options and ResourcesNew Options and ResourcesFor Getting Jobs DoneFor Getting Jobs DoneFor Getting Jobs DoneFor Getting Jobs Done

Seybold Enterprise Options.dsf Copyright Richard L. Ballard 2003

27

Enterprise Knowledge Stacks

Publishers Update Overlay

Aftermarket OverlaysIntegration OverlaysLatest Information Overlays

Requirement & Assumption Overlays

User Work Products & Overlays LayerUser Work-In-Progress Layers

Validated Workgroup Baseline

USER'S WORKING LEVELS

Published Reference StackProprietary Knowledge Assets

WORKGROUP SHARED ASSETS

CORPORATE KNOWLEDGE ASSETS

Virtual Integration ofVirtual Integration ofVirtual Integration ofVirtual Integration ofWorks-in-ProgressWorks-in-ProgressWorks-in-ProgressWorks-in-Progress

KnowledgeKnowledgeKnowledgeKnowledgeWorkersWorkersWorkersWorkers

CommercialCommercialCommercialCommercialKnowledgeKnowledgeKnowledgeKnowledgeProvidersProvidersProvidersProviders

ImmediateImmediateImmediateImmediateOn-lineOn-lineOn-lineOn-line

InformationInformationInformationInformationUpdatesUpdatesUpdatesUpdates

HIGH PERFORMANCEHIGH PERFORMANCEHIGH PERFORMANCEHIGH PERFORMANCEKNOWLEDGE WEBKNOWLEDGE WEBKNOWLEDGE WEBKNOWLEDGE WEB

Seybold Enterprise Stack.dsf Seybold Enterprise Stack.dsf Seybold Enterprise Stack.dsf Seybold Enterprise Stack.dsf Copyright Richard L. Ballard 2003Copyright Richard L. Ballard 2003Copyright Richard L. Ballard 2003Copyright Richard L. Ballard 2003

28

BUILDER As ResourceBUILDER As ResourceBUILDER As ResourceBUILDER As ResourceIntegrator to Unlimited SizeIntegrator to Unlimited SizeIntegrator to Unlimited SizeIntegrator to Unlimited Size

StackDirectory

SessionDirectory

ExternalRelationship

File

ExternalRelationship

File

Dynamically LinkedSESSION OVERLAY

STACKMASTER OVERLAY

Static or DynamicLayers & Overlays

CompressedStack File

CompressedStack Files

Stored in User'sStored in User'sStored in User'sStored in User'sClient MachineClient MachineClient MachineClient Machine

Stored in ClientStored in ClientStored in ClientStored in Clientand/orand/orand/orand/or

Fast (< 5 sec.)Fast (< 5 sec.)Fast (< 5 sec.)Fast (< 5 sec.)Responding ServerResponding ServerResponding ServerResponding Server

BUILDER stacks specific VirtualKnowledge Layers located anywhere

on a Fast Responding network

Seybold Knowledge Integration.dsf Copyright Richard L. Ballard 2003

29

understand choices & Outcomesunderstand choices & Outcomesunderstand choices & Outcomesunderstand choices & Outcomeswhen there are no right answerswhen there are no right answerswhen there are no right answerswhen there are no right answers

Constraint BrowsersConstraint BrowsersConstraint BrowsersConstraint Browsers

Sebold Knowledge Superiority.dsf Copyright Richard L. Ballard 2002

INFORMATION --INFORMATION --INFORMATION --INFORMATION --matching Your situationmatching Your situationmatching Your situationmatching Your situation

Current Situation

DesiredOutcome

Expected Impacts

UNEXPECTEDIMPACT

Experimentingwiith this Choice

Modeled theoryModeled theoryModeled theoryModeled theoryas a as a as a as a systemsystemsystemsystemof rationalof rationalof rationalof rational

ConstraintsConstraintsConstraintsConstraints

THEORY THEORY THEORY THEORY follows throughfollows throughfollows throughfollows throughoptions to impactsoptions to impactsoptions to impactsoptions to impacts

UnderstandingUnderstandingUnderstandingUnderstandingYour Boss's ChoicesYour Boss's ChoicesYour Boss's ChoicesYour Boss's Choices

Your ChoicesYour ChoicesYour ChoicesYour ChoicesYour Employee's ChoicesYour Employee's ChoicesYour Employee's ChoicesYour Employee's Choices

1111

2222

30

Given an AgeGiven an AgeGiven an AgeGiven an Age

Where Knowledge Need Never DieWhere Knowledge Need Never DieWhere Knowledge Need Never DieWhere Knowledge Need Never Die

What Do We Intend Now to Build What Do We Intend Now to Build What Do We Intend Now to Build What Do We Intend Now to Build

Make It Worthy of This Gift

Creating and Using

Systems That Know- Anything -

August 2008August 2008August 2008August 2008

Dr. Richard L. BallardDr. Richard L. BallardDr. Richard L. BallardDr. Richard L. BallardChief ScientistChief ScientistChief ScientistChief Scientist

Focus of Technical BriefingFocus of Technical BriefingFocus of Technical BriefingFocus of Technical Briefing

1. Origin of a Precise Theory of KnowledgeDeveloped Over the Period 1987-1993

By Dr. Richard Ballard

2. Development of That Theory Into A ThirdGeneration Knowledge Tool

Advanced Engineering 1993 - 2004Breakthroughs 2005 - 2008

3. Ballard / Shannon Limit SuccessAbility to Store Unlimited Knowledge

In Absolute Minimum Space

4. Constraint Browsing -- AxiologyPortraying And Judging EveryHuman Value And Necessity

Briefing Focus Knowledge Foundations

Formulating A PreciseFormulating A PreciseFormulating A PreciseFormulating A PreciseTheory of KnowledgeTheory of KnowledgeTheory of KnowledgeTheory of Knowledge

Knowledge = Knowledge = Knowledge = Knowledge = TheoryTheoryTheoryTheory + + + + InformationInformationInformationInformation

Dr. Richard L. Ballard 1987-1993

Evolutionary Biological Knowledge Types.dsf Copyright Richard L. Ballard 1998-2003

Knowledge As Evolutionary ScienceKnowledge As Evolutionary ScienceKnowledge As Evolutionary ScienceKnowledge As Evolutionary Science

1 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010


1

101

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 9

1010

1011

1012

1013

1014


eural Bits)

Adapted from The Dragons of Eden , Carl Sagan, 1977

Humans

Mammals

Reptiles

Jellyfish

Virus


Amphibians

ImplyingKnowledge TypeFromStorage Type

Genes vs Brains

Ac

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Instinctive DNA Inheritance

SENSE ORGAN receipt ofInformation produces

physiologicalsituation awareness

-- with or without a brain.

BRAINLESS animals reactusing their instinctive dna

programs -- to succeed or die.

Badly adapted species die out.



1 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010


1

101

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 9

1010

1011

1012

1013

1014

Brain Knowledge Storage (Neural Bits)

Adapted from The Dragons of Eden , Carl Sagan, 1977

Humans

Mammals

Reptiles

Jellyfish

Virus


Amphibians

ImplyingKnowledge TypeFromStorage Type

Genes vs Brains

Ac

qu

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Kn

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Instinctive DNA Inheritance

They constantly adapt"brain content (Theory)"with no need to change

the host's biological form.

BRAIN memories model,store, and teach

successful behaviorsas "lessons learned."

1

10 1

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 9

10 10

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10 21

101 10181 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 10151016 1017

Human

Mammals

AmphibiansJellyfish

ProtozoaAlgae

Virus


Workgroups


Bacteria

Reptiles

Turn TowardAll Theories --

All Knowledge

Logic+

Information

SOFTWARE

HARDWARE

DOS Windows 98

486 PC

PentiumLaptop

386 PC

KNOWLEDGE AGEREQUIREMENTS

KnowledgeKnowledgeKnowledgeKnowledgeCodes &Codes &Codes &Codes &TheoryTheoryTheoryTheory

ModelingModelingModelingModelingGapGapGapGap

Intelligent Animals Embrace Many Intelligent Animals Embrace Many Intelligent Animals Embrace Many Intelligent Animals Embrace Many """"BehaviorBehaviorBehaviorBehaviorPatterns"Patterns"Patterns"Patterns" For Their Self-evident SuccessFor Their Self-evident SuccessFor Their Self-evident SuccessFor Their Self-evident Success

1. Logical Self-consistency insures machine-like behavior, following external mandates ABSOLUTELY.

2. Their proofs possess no intrinsic measures of: efficiency, resource requirement, or complexity costs.

Copyright Richard L. Ballard 2006Irrelevance of Logical Reasoning.dsf

3. Badly matched to a problem, their costs CREATE "non-computability."

Life Life Life Life vs.vs.vs.vs. LogicLogicLogicLogic

1

10 1

10 2

10 3

10 4

10 5

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10 7

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10 10

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10 21

101 10181 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 10151016 1017

Human

Mammals

AmphibiansJellyfish

ProtozoaAlgae

Virus


Workgroups


Bacteria

Reptiles

Turn TowardAll Theories --

All Knowledge

Logic+

Information

SOFTWARE

HARDWARE

DOS Windows 98

486 PC

PentiumLaptop

386 PC

KNOWLEDGE AGEREQUIREMENTS



Intelligent Animals Embrace Many Intelligent Animals Embrace Many Intelligent Animals Embrace Many Intelligent Animals Embrace Many """"BehaviorBehaviorBehaviorBehaviorPatterns"Patterns"Patterns"Patterns" For Their Self-evident SuccessFor Their Self-evident SuccessFor Their Self-evident SuccessFor Their Self-evident Success

1. Extremely resource aware, their many possible goals are all Intentional, Competitive, Success-oriented, and often achievable in multiple ways.

2. They reject options that do not match their situation or go against theories they trust.


3. They expect most choices are not provably right or wrong, seek to enumerate all options, and predict the consequences of each option before deciding.

Abstraction"the act of consideringsomething as a general

quality or characteristic,apart from concrete

realities, specific objects,or actual instances"

-- Random House Dictionary

Aggregation"collection of particularsinto a whole mass or sum"


Everything Real &Observable

EverythingImaginable

Every "Science" Every "Fantasy"

Copyright Richard L. Ballard 1998-2007Imagination & Reality.dsf

PracticalRationalism

ConceptsCategories

Epistemology

TheologyPure Intellect

Ontology

Axiomologies

Imagination

Form

Beauty

TruthLogic

Mathematics

Natural Laws

Requirements

Design

Faith

Mark 3 Top-MostPrimitives

(HyptheticalModels)

(Acquired Models)

Ideas Theories Models Formalisms

AbsoluteBeing

PerfectIntelligence

Ontological Primitives Cosmology

Chemistry

Biology


EnergyMatter

Machines

Planets

Animals

Galaxies

Local Groups

Societies

Particles

PhysicalUniverse

Sense DataPhenomena ObservablesEvents

Guanine

SKELETALDIGESTIVE

CIRCULATORYNERVOUS

Conceptualizing and OrganizingConceptualizing and OrganizingConceptualizing and OrganizingConceptualizing and OrganizingAll All All All of Imagination and Realityof Imagination and Realityof Imagination and Realityof Imagination and Reality

ENTITY

N-ARYRELATIONSHIP

THEORY-BASED MEDIATING STRUCTURE

n=2 n>2

1

2

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"Knowledge"Knowledge"Knowledge"KnowledgeTheory-basedTheory-basedTheory-basedTheory-based

Semantic Web"Semantic Web"Semantic Web"Semantic Web"


Metaphysics

TheologyCosmology

ChemistryBiology


Energy

Matter


Sense Data

Phenomena

Epistemology

Concepts

MaterialsMachines

LANGUAGE

ARTCategories

Exemplars

Realistic

Beauty

Truth

Pure Intellect

NaturalLanguages

Mathematics

Logic

Measurement

IconsPhotographs

SYMBOLS& CODES

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observed fact, material existence,and recorded measurementPersistent

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Physics


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Knowledge Theoretic Representation.dsf

Models

Situation ConstrainedNavigation of Every

Accepted Fact, Theory, &Predictable Decision Impact

Model Instance Codes.dsf Copyright Richard L. Ballard 2003



PhysicsMetaphysics

Model

Inst

a 1 - Nnces

Instance 0PhysicalConcept

Inst

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ModelInstance 0

Meta-PhysicalConcept

PhysicalUniverse

AbsoluteBeing

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Intelligence

DataTypeModel

Inst

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Instance 0

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of Knowledge

Model Model Model Model - - - - InstanceInstanceInstanceInstance Concept Codes Concept Codes Concept Codes Concept Codes Are Unique and Identical Are Unique and Identical Are Unique and Identical Are Unique and Identical In All Languages In All Languages In All Languages In All Languages




A CONCEPT (model-instance) appearsonly once in any semantic web, its unique code locates it instantly -- without search

X

"Information, Structure, Inference -- A Physical Theory of Knowledge and Computation"

Dr. Richard L. Ballard, 1993

Theory-basedTheory-basedTheory-basedTheory-based

ProbabilityProbabilityProbabilityProbabilityof Predictingof Predictingof Predictingof PredictingOutcomes forOutcomes forOutcomes forOutcomes forEvery ChoiceEvery ChoiceEvery ChoiceEvery Choice

Probability ofProbability ofProbability ofProbability ofRecognizingRecognizingRecognizingRecognizing

SituationSituationSituationSituationCorrectlyCorrectlyCorrectlyCorrectly

Probability ofProbability ofProbability ofProbability ofKnowing EveryKnowing EveryKnowing EveryKnowing EveryOption OutcomeOption OutcomeOption OutcomeOption Outcome

Before DecisionsBefore DecisionsBefore DecisionsBefore DecisionsAre MadeAre MadeAre MadeAre Made

KnowingKnowingKnowingKnowingCurrent orCurrent orCurrent orCurrent orHypotheticalHypotheticalHypotheticalHypotheticalSituationSituationSituationSituation

Physical Theory of Knowledge & ComputationPhysical Theory of Knowledge & ComputationPhysical Theory of Knowledge & ComputationPhysical Theory of Knowledge & Computation

Physical Event of"Thought" or"Execution"

SemanticSemanticSemanticSemanticWebWebWebWeb

RealityRealityRealityReality

SITUATION PREDICTIVE WEB DECISION IMPACT

P(a, b, c ,... x, y, z) P(a, b, c ,... | ... x, y, z) P( ... x, y, z)==== ××××


Probabilistic "Practical Rationality"Probabilistic "Practical Rationality"Probabilistic "Practical Rationality"Probabilistic "Practical Rationality"

Probabilistic Knowledge Theory A.dsf

X



GoalsGoalsGoalsGoalsEducationEducationEducationEducationResponsibilityResponsibilityResponsibilityResponsibilityRequirementsRequirementsRequirementsRequirementsIntentIntentIntentIntent

TimeTimeTimeTimeRelationRelationRelationRelation

ResourceResourceResourceResourceOpportunityOpportunityOpportunityOpportunity

ActionActionActionAction





Probability ofProbability ofProbability ofProbability ofKnowing EveryKnowing EveryKnowing EveryKnowing EveryOption OutcomeOption OutcomeOption OutcomeOption Outcome

Before DecisionsBefore DecisionsBefore DecisionsBefore DecisionsAre MadeAre MadeAre MadeAre Made



Physical Event of"Thought" or"Execution"


RealityRealityRealityRealitya' priori a' posterioriDegrees of Freedom & Constraint



a, b, c, ... ... x, y, z




Theory-basedTheory-basedTheory-basedTheory-basedSemantic Semantic Semantic Semantic WebWebWebWeb RealityRealityRealityReality


P(a, b, c ,... x, y, z) P(a, b, c ,... | ... x, y, z) P( ... x, y, z)==== ××××Fundamental Ultimate Limit Measures

KNOWLEDGE ==== THEORYTHEORYTHEORYTHEORY ++++ INFORMATIONINFORMATIONINFORMATIONINFORMATION

Copyright Knowledge Foundations 2006Quantitative Hard Science.dsf

Knowledge As AKnowledge As AKnowledge As AKnowledge As AQuantitative Hard ScienceQuantitative Hard ScienceQuantitative Hard ScienceQuantitative Hard Science




a' priori a, b, c, ... ... x, y, za' posterioriDecision Success P(task)Decision Success P(task)Decision Success P(task)Decision Success P(task)

Theory-basedTheory-basedTheory-basedTheory-basedSemantic Semantic Semantic Semantic WebWebWebWeb RealityRealityRealityReality


P(a, b, c ,... x, y, z) P(a, b, c ,... | ... x, y, z) P( ... x, y, z)==== ××××Fundamental Ultimate Limit Measures




Knowledge Theory-basedUltimate Minimum

Decision Resource Cost

-log{P(a, b, c,...x, y, z)}

ShannonInformation Bandwidth

& Storage Limit Cost

-log{P(...x, y, z)}

Ballard Education, Web Certification,

& Theory Capture Limit Cost

-log{P(a, b, c,...|....x, y, z)}

Ballard Education, Web Certification,

& Theory Capture Limit Cost

-log{P(a, b, c,...|....x, y, z)}Theory predicts thatcosts can & will scale

proportionally toInformation Content

Theory provides performance-based measures comparing

Education, Theory Capture, &Knowledge Creation investment

Theory links task specificsuccesses to most effectivetrade-offs in training, theorycreation, & technology use




a' priori a, b, c, ... ... x, y, za' posterioriDecision Success P(task)Decision Success P(task)Decision Success P(task)Decision Success P(task)

On Creating A Third On Creating A Third On Creating A Third On Creating A Third GenerationGenerationGenerationGeneration

Knowledge ToolKnowledge ToolKnowledge ToolKnowledge ToolAdvanced Engineering 1993 - 2004

Breakthroughs 2005 - 2008

Structured Storage

SERVERSERVERSERVERSERVERPROCESSORSPROCESSORSPROCESSORSPROCESSORS

PrimitiveCache

Mk 3Full

Server

FoundationsEXE

ConceptCache

Mk 3ConceptHandler

&In-ProcServer

FoundationsDLL

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"Words"

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Architecture ofArchitecture ofArchitecture ofArchitecture ofConcept Knowledge FlowConcept Knowledge FlowConcept Knowledge FlowConcept Knowledge Flow


Mark 3 Knowledge Flows 6 8x11.dsf

OPEN MFC COMPONENT

Base Class

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Formatting& EditorialRescaling

1

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4

5MARK 3 ALPHA BACKEND MARK 3 ALPHA BACKEND MARK 3 ALPHA BACKEND MARK 3 ALPHA BACKEND

C:/

FOUNDATIONS KB LAYER 1

KB LAYER 2

SOURCES

EDIT STACK

ONTOLOGY OVERLAYDEV HISTORY OVERLAY

DOC1DOC2DOC3

EDITOR1

EDITOR3EDITOR2

DEVTOOLS

USERTOOLS

COMMON FILES

KB LAYER 3

KB STACK A

KB STACK B

FOUNDATIONS Directory Tree

WORKSPACES

USER1USER2USER2

1024 x 670Client Area

Sta rt Type to se arc h 11 :58 PMMedic al Gu ide Foundations BRO W SER

File View W indow Help Concep t:Concep t:Concep t:Concep t: N et work/ TreeN et work/ TreeN et work/ TreeN et work/ Tree Views :Views :Views :Views : TimeTimeTimeTime Cl us terCl us terCl us terCl us ter ConceptConceptConceptConceptNetworkNetworkNetworkNetwork

Foundat ions Browser - - [Medical Guide] Foundat ions Browser - - [Medical Guide] Foundat ions Browser - - [Medical Guide] Foundat ions Browser - - [Medical Guide]

SELEC TED CONC EPTSELEC TED CONC EPTSELEC TED CONC EPTSELEC TED CONC EPT RE LA TIONSH IP SRE LA TIONSH IP SRE LA TIONSH IP SRE LA TIONSH IP S PA TH NA MESPA TH NA MESPA TH NA MESPA TH NA MES

ControlDashboard

IMA G ESIMA G ESIMA G ESIMA G ES

DE FI NIT IONSDE FI NIT IONSDE FI NIT IONSDE FI NIT IONS

CenteredPrimaryOptionLeft

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Left - mostSecondary

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RightSecondary

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1024 x 670Client Area

Sta rt Type to search 11 :58 PMMe dical Guide Foundations BROW SER

File View W indow He lp C oncept:C oncept:C oncept:C oncept: N et wo rk/Tr eeN et wo rk/Tr eeN et wo rk/Tr eeN et wo rk/Tr ee Views :Views :Views :Views : TimeTimeTimeTime Cl ust erCl ust erCl ust erCl ust er ConceptConceptConceptConceptNet workNet workNet workNet work

Foundations Brows er - - [Medical Guide] Foundations Brows er - - [Medical Guide] Foundations Brows er - - [Medical Guide] Foundations Brows er - - [Medical Guide]

SELEC TED CONCEP TSELEC TED CONCEP TSELEC TED CONCEP TSELEC TED CONCEP T RELA TIONSHIPSRELA TIONSHIPSRELA TIONSHIPSRELA TIONSHIPS PAT H NAM ESPAT H NAM ESPAT H NAM ESPAT H NAM ES

ControlDashboard

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Patterns of ThoughtDr. Richard L. Ballard

ENT ITYN-ARY

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Knowledge Science & Engineering

Dr. Richard L. BallardDecember 2004

FIRST COURSE IN KNOWLEDGE ENGIN EERING

Creating Systems That Know

CREATING A NEW SC IENCE 1970-2005

KNOWLEDGE FOUNDATIONSMARK 3 VERSION 1 ALPHA

BROWSER Fact Sheets

COPYRIGHTCOPYRIGHTCOPYRIGHTCOPYRIGHTDr. Richard L. Ballard

February 2007 - September 2007

CODING MOST SECRET FUTURE ENTERPRISE & INVENTION

ArchitecturesArchitecturesArchitecturesArchitecturesArchitecturesArchitecturesArchitecturesArchitecturesMark 3 vs ConventionalMark 3 vs ConventionalMark 3 vs ConventionalMark 3 vs ConventionalMark 3 vs ConventionalMark 3 vs ConventionalMark 3 vs ConventionalMark 3 vs Conventional

Databases

Operating System

• KFI KFI KFI KFI ---- Mark 3Mark 3Mark 3Mark 3

Theory-based Knowledge Integration• Conventional Layer CakeConventional Layer CakeConventional Layer CakeConventional Layer Cake

Code, Structure & Object Integration

©Knowledge Foundations, Inc./D.L. Thomas Open source diagram

Copyright KNOWLEDGE RESEARCH

1990-2005

MARK 2 BUILDERPatterns of Thought

1990-2001

Congress

InternationalOrganizations

AlliedGovernments

GovernmentOrganization

Political &Economic

Base

IntelligenceOrganizations

DoDOrganization

CongressionalCommittees

ManufacturingAssociations

MilitaryFacilities

NavyOrganization

Air ForceOrganization

Major DefenseContractors

MajorSubcontractors

ThreatGeography

OperationalForces

MissionRequirements

ProcurmentPrograms

Cost &Technology

RiskModeling

ThreatScenarios

OperationalConcepts

R&DPrograms

Major WeaponSystems

ManufacturingTechnology

ThreatDatabases

WarfareRequirements

TechnologyRequirements

TechnologyIntegrationModel Base

MajorSubsystems

MissionTask Analysis

DesignModel Base

TechnologyTestbeds

SystemPerformanceRequirements

SystemTest

Requirements

TrainingObjectives

Crew CenteredRequirements

MissionSimulation

SystemSimulation

SystemTest

Facilities

TrainingRequirements

TrainingFacilities

AlgorithmicModel Base

PerformanceAnalysis

Model Base

Publishers Update OverlayAftermarket OverlaysIntegration OverlaysLatest Information Overlays








Integrate KnowledgeProduced By

ALL PUBLISHERS

NEED TO MATCH CONCEPTS ACROSSNEED TO MATCH CONCEPTS ACROSSNEED TO MATCH CONCEPTS ACROSSNEED TO MATCH CONCEPTS ACROSSALL KNOWLEDGE SOURCESALL KNOWLEDGE SOURCESALL KNOWLEDGE SOURCESALL KNOWLEDGE SOURCES

Industry-wide Asset Integration.dsf Copyright Richard L. Ballard 1993-2008

Theory-basedTheory-basedTheory-basedTheory-basedSemanticsSemanticsSemanticsSemantics Mark 3, Version 2: Design Objective

INDUSTRY STANDARDINDUSTRY STANDARDINDUSTRY STANDARDINDUSTRY STANDARD

To Begin The Process Of To Begin The Process Of To Begin The Process Of To Begin The Process Of AssessingAssessingAssessingAssessingThe Impact Of Every SituationThe Impact Of Every SituationThe Impact Of Every SituationThe Impact Of Every Situation

Congress


AlliedGovernments


Political &Economic

Base


DoDOrganization



MilitaryFacilities

NavyOrganization



MajorSubcontractors

ThreatGeography

OperationalForces

MissionRequirements

ProcurmentPrograms

Cost &Technology

RiskModeling

ThreatScenarios

OperationalConcepts

R&DPrograms

Major WeaponSystems


ThreatDatabases

WarfareRequirements



MajorSubsystems


DesignModel Base

TechnologyTestbeds


SystemTest

Requirements

TrainingObjectives


MissionSimulation

SystemSimulation

SystemTest

Facilities


TrainingFacilities


PerformanceAnalysis

Model Base

1

2

3

Sit uation

4

Locating

5

6

Tar get

7

Wha t is Ther e?

8

Tak es ou tJ-5 too

Who has got one?

20 MatchingNAT O Jammer

Band19

Just the Bad Gu ys

18Whoelse?

17Wha tfrequ enc ies?

1615

Rad ar? 15Armored,

Shoots bigger ,farther &

faster than us

Top Gu n

Can he see us?

14 13Hea rt ofDa rkness

12Where is hisweak ness?

Or ga nization& Equipment

11Nightmar e, WhenFlying low!

10Who is shooting

at Us?

9

Who c arriesthis system?

22 Wha t elsedo you ca rry?23 I wa nt this guy

with u s!

Mou nt theband 10

jammer pod!

24

ACE CVN-77Program ManagementKnowledge Base 1998

Office of theSecretary of Defense

Countries

Instance 7:Republic of Iraq

Rel. Instance 3:Country/Military

Facilities

Instance 3:Balad Airfield

Rel. Instance 2:Facitities/Sub-

Components

Rel Instance 4:Facitities/Sub-

Components

Instance 2:ZSU-23 Battery

Model: COUNTRIES

Model:MILITARY AIRFIELDS

Model: SURFACEAIR DEFENSES

Model:AAA BATTERIES

Rel. Model:COUNTRY/MILITARYFACILITIES

Rel. Model:FACITITIES/SUB-COMPONENTS

Rel. Model: TABLE OF ORGANIZATION & EQUIPMENT

Rel:JAM/FREQ

7

3

2

3

4 8

Instance 8:Balad Surface

Air Defenses

149 25 2

Rel Instance 1:Organization /

Equipment


Equipment


Equipment

Instance 4:ZSU-23 Platoon

Model: AAA PLATOONS

Instance 9:ZSU-23 Gun

Model:AAA GUNSInstance 4:

Gun Dish Fire Control Radar

Model:FIRE CONTROL RADARS

5

Instance 2:Source/Freq

Rel:SOURCE/FREQ

42

Instance 8:Channel J-6

Model:CHANNELS

35 8

Instance 3:Jam/Freq

Instance 5:ALQ-99 Band 10

Model:JAM BAND

Instance 1: ALQ-99F

Air For ce Na vy21

Rel:SYS/BAND

Instance 5:Pod/Freq

Rel Model: SYSTEM/SUB-SYSTEMInstance 2: System/Sub-Sys

Model:JAMMER SYSTEM

55 21 2

Instance 5: System/Sub-Sys

Model:EA-6B WEAPONS

Instance 5: Jammer Loadout

Model:EA-6B Prowler

Instance 2: The Answer to Any Question isthe Whole "Chain of Reasoning"

8

3 4

2

1

756

9

12

10 11

13

15 16 17

2122

2324

18

1920

14


1990-2005


1990-2001

SITUATION P( ... x, y, z)

... x, y, z SITUATION

N = 3ASSUMPTIONS:... x, y, z

Copyright Knowledge Foundations 2006Semantic Web 1.dsf

To Assess Every Possible OptionTo Assess Every Possible OptionTo Assess Every Possible OptionTo Assess Every Possible OptionAnd Decision ImpactAnd Decision ImpactAnd Decision ImpactAnd Decision Impact

Congress


AlliedGovernments


Political &Economic

Base


DoDOrganization



MilitaryFacilities

NavyOrganization



MajorSubcontractors

ThreatGeography

OperationalForces

MissionRequirements

ProcurmentPrograms

Cost &Technology

RiskModeling

ThreatScenarios

OperationalConcepts

R&DPrograms

Major WeaponSystems


ThreatDatabases

WarfareRequirements



MajorSubsystems


DesignModel Base

TechnologyTestbeds


SystemTest

Requirements

TrainingObjectives


MissionSimulation

SystemSimulation

SystemTest

Facilities


TrainingFacilities


PerformanceAnalysis

Model Base

1

2

3

Sit uation

4

Locating

5

6

Tar get

7

Wha t is Ther e?

8

Tak es ou tJ-5 too

Who has got one?

20 MatchingNAT O Jammer

Band19

Just the Bad Gu ys

18Whoelse?

17Wha tfrequ enc ies?

1615

Rad ar? 15Armored,

Shoots bigger ,farther &

faster than us

Top Gu n

Can he see us?

14 13Hea rt of

Da rkness

12Where is hisweak ness?

Or ga nization& Equipment

11Nightmar e, WhenFlying low!

10Who is shooting

at Us?

9

Who c arriesthis system?

22 Wha t elsedo you ca rry?23 I wa nt this guy

with u s!

Mou nt theband 10

jammer pod!

24



Countries



Facilities



Components


Components


Model: COUNTRIES



Model:AAA BATTERIES




Rel:JAM/FREQ

7

3

2

3

4 8


Air Defenses

149 25 2


Equipment


Equipment


Equipment


Model: AAA PLATOONS





5


Rel:SOURCE/FREQ

42


Model:CHANNELS

35 8

Instance 3:Jam/Freq


Model:JAM BAND

Instance 1: ALQ-99F

Air For ce Na vy21

Rel:SYS/BAND

Instance 5:Pod/Freq


Model:JAMMER SYSTEM

55 21 2


Model:EA-6B WEAPONS


Model:EA-6B Prowler


8

3 4

2

1

756

9

12

10 11

13

15 16 17

2122

2324

18

1920

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1990-2005


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PREDICTIVE WEB P(a, b, c ,... | ... x, y, z)



CONOPSOption #1

N = 4DECISIONS:

THEORYN = 7

Decisivedegrees

offreedom

Copyright Knowledge Foundations 2006Semantic Web 2.dsf

On AchievingOn AchievingOn AchievingOn AchievingBallard / ShannonBallard / ShannonBallard / ShannonBallard / Shannon

Limit SuccessLimit SuccessLimit SuccessLimit SuccessAbility to Store Unlimited Knowledge

In Absolute Minimum Space

Google employs 450,000 servers, deployed in 25+ world locations, processing 20 petabytes per day.

Google processes its data on a standard machine cluster node consisting two 2 GHz Intel Xeon processors with Hyper-Threading enabled, 4 GB of memory, two 160 GB IDE hard drives and a gigabit Ethernet link.

IBM mainframes build atop a myriad of database engines, sourced from a variety of DBMS vendors.

IBM mainframes focus on critical business applications such as: Human Resource Management (HR), Customer Relationship Management (CRM), Accounting, Supply Chain Management etc.

Large databases support 5,000 to 20,000 tables/fields to represent 1000s of abstracted concepts

Servers are responsible for using 0.8% of world energy supply and 1.2% of US energy (2005).

Mainframe, Blade Servers & Software

Unique Mark 3 Knowledge Platform

Mark 3 is built upon absolute minimum, Shannon Limit size, andunlimited knowledge capacity. No other tool can do this.

Mark 3 is built upon a recognized theory of knowledge. It producesa complete description of all the information and theory needed.

Mark 3 supports a non-object oriented, theory-based description ofknowledge, capable of describing anything, Real or Imagined.

Mark 3 moves directly to content. It employs no indexing or search.

Mark 3 is capable of describing every relationship between theoriesand objects.

Mark 3 enables the complete development and evolution of any andall knowledge systems.

Mark 3 creates a Race to Reference Dominance, building many layersof knowledge that can grow collectively to unlimited size.

Knowledge Layers from Many Sources

EmploysEmploysEmploysEmploysConstraint Browsing Constraint Browsing Constraint Browsing Constraint Browsing

---- Axiology Axiology Axiology Axiology ----Portraying And Judging Every

Human Value And Necessity

Constraint Browsing

Medical DiagnosisMedical DiagnosisMedical DiagnosisMedical Diagnosis

Out Take: American College Of Physicians– Home Medical Guide

_________________

Knowledge Browser identifies 13+ levels of diagnosticsfor the natural language question: "I don’t feel well."

This knowledge example from: "Complete Home Medical Guide." includes 8 levels not show, but listed above.

File View Window Help

Concept: Constraint Browser Views: Time Cluster ConceptConstraint

Foundations Browser -- [Medical Guide]

Start Type to search 11:58 PMMedical Guide Foundations BROWSER

No CorrelationsKnown

Weak Correlation'sNeglected

Remaining StrongCorrelations

ParametersChosen

RASH NoRash

Flat Dark RedSpots, DoNot Fade

Dull RedSplotches,

Do Fade

WidespreadIchy, Blistery

Rash

Rash Spreadsfrom Central

Red Spot

Bright RedRash Affecting

Cheeks

Light RedRash on

Trunk or Face

SevereHeadache

Mild orNo

Headache

NoneAbove

Meningitus Drug Alergy Thrombo-cytopenia Measles Scarlet

FeverChicken

PoxLyme

DiseaseParvo-virus Rubella Pneumonia Acute

Bronchitis

Emergency UrgentHelp

Bring DownFever

CallDoctor in24 hours

MedicalHelp

Self-HelpBring Down

Fever

Self-HelpHome

PregnancySore

ThroatContinue

Prescription

NOFever

Above100 F (38 C)Temperature

Time

Diagnosis

Urgency

Temperature

Rash

Indication #1

1 hr..5.4.3.2.1 1 day.5.4.3.2 1 week.5.4.3.2 1 month.5.4.3.2 1 year.5.4.3.2 10 year5432 10050403020

? ? Not Feeling WellStart by choosing your costs first.

Control DashboardSELECTED CONCEPT

Diagnosis -- Not Feeling Well

RELATIONSHIPS

Diagnostics 3-13

PATH NAMES


Clicking on “Emergency” instantly limits the case being considered. The screen shows “Meningitus” as the primary threat. It indicates

only minutes to hours to survive.File View Window Help







ParametersChosen

RASH NoRash


SevereHeadache

Meningitus

Emergency


1 hr..5.4.3.2.1 1 day.5.4.3.2

?

Time

Diagnosis

Urgency

Temperature

Rash

Indication #1

? ? Not Feeling WellControl Dashboard

SELECTED CONCEPT

Emergency

RELATIONSHIPS

Diagnostics 7

PATH NAMES


Selecting Parameters Chosen accepts all those 4 rows of conditions assumed. Then it chooses to look higher at the less significant symptoms.








ParametersChosen

? ? ?

RASH NoRash


SevereHeadache

Meningitus

Emergency


1 hr..5.4.3.2.1 1 day.5.4.3.2Time

Diagnosis

Urgency

Temperature

Rash

Indication #1

Not Feeling Well

SELECTED CONCEPT

Emergency

RELATIONSHIPS

Diagnostics 7

PATH NAMES


PARAMETERS CHOSEN

Temperature -- Above 100FUrgency -- EmergencyDiagnosis -- MeningitusTime -- 6 min -> 1 day

Control Dashboard

As known results disappear from sight, the higher and less significant diagnostic choices are drawn

To help confirm the emergency diagnosis -- Meningitus.File View Window Help







ParametersChosen

? ? ?

Rash

Indication #1

RASH NoRash


SevereHeadache

Indication #2

Drowsiness& Confusion

Severe DislikeBright Lights

Pain BendingHead Forward

Nausea orVomiting

Not Feeling Well

SELECTED CONCEPT

Emergency

RELATIONSHIPS

Diagnostics 7

PATH NAMES


PARAMETERS CHOSEN

Temperature -- Above 100FUrgency -- EmergencyDiagnosis -- MeningitusTime -- 6 min -> 1 day

Control Dashboard

As a backup, the slightly less “Urgent” choice is examined also.Here the critical time values extend to 2 weeks and 7-9 other

diagnostic choices appear.








ParametersChosen



Urgent

RELATIONSHIPS

Diagnostics 7- 9

PATH NAMES


RASH NoRash

Rash

1 hr..5.4.3.2.1 1 day.5.4.3.2 1 week.5.4.3.2 .2Time

Drug Alergy Thrombo-cytopenia Pneumonia

Diagnosis

UrgentUrgency

Above100 F (38 C)TemperatureTemperature


SevereHeadache

NoHeadache

MildHeadache

Indication #1

Once a diagnosis is determined, users can pursue treatment optionsat "the speed of thought."

Knowledge of every possibility is immediately available to every potential patient.








ParametersChosen

RASH NoRash


Dull RedSplotches,Do Fade

WidespreadIchy, Blistery

Rash

Rash Spreadsfrom Central

Red Spot

Bright RedRash Affecting

Cheeks

Light RedRash on

Trunk or Face

SevereHeadache

Mild orNo

Headache

NoneAbove

Meningitus Drug Alergy Thrombo-cytopenia Measles Scarlet

FeverChicken

PoxLyme

DiseaseParvo-virus Rubella Pneumonia Acute

Bronchitis

Emergency UrgentHelp

Bring DownFever

CallDoctor in24 hours

MedicalHelp

Self-HelpBring Down

Fever

Self-HelpHome

PregnancySore

ThroatContinue

Prescription

NOFever


Time

Diagnosis

Urgency

Temperature

Rash

Indication #1

1 hr..5.4.3.2.1 1 day.5.4.3.2 1 week.5.4.3.2 1 month.5.4.3.2 1 year.5.4.3.2 10 year5432 10050403020




RELATIONSHIPS

Diagnostics 3-13

PATH NAMES


Presenter:Dr. Richard L. Ballard

Chief ScientistKnowledge Foundations

1

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"v"

"w"

"x"

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N-DimensionalSITUATION AWARENESSSITUATION AWARENESSSITUATION AWARENESSSITUATION AWARENESS

Predicts Theory-basedDECISION OPTIONDECISION OPTIONDECISION OPTIONDECISION OPTION

CONSTRAINT TRADE-OFFSCONSTRAINT TRADE-OFFSCONSTRAINT TRADE-OFFSCONSTRAINT TRADE-OFFS

KnowledgeFOUNDATIONS

Copyright Richard L. Ballard 2006

N = 7

N-aryTHEORY-BASEDRELATIONSHIP

BUNDLE

N = 8

Systems That KnowSystems That KnowSystems That KnowSystems That Know -- -- -- -- ULTIMATE INNOVATIONSULTIMATE INNOVATIONSULTIMATE INNOVATIONSULTIMATE INNOVATIONS

Semantic Web"Chain of Reasoning"

www.KnowledgeFoundations.com

2

Presenter:Presenter:Presenter:Presenter:Dr. Richard L. BallardDr. Richard L. BallardDr. Richard L. BallardDr. Richard L. Ballard

Chief ScientistChief ScientistChief ScientistChief ScientistKnowledge FoundationsKnowledge FoundationsKnowledge FoundationsKnowledge Foundations

3

KNOWLEDGEKNOWLEDGEKNOWLEDGEKNOWLEDGEMANAGEMENTMANAGEMENTMANAGEMENTMANAGEMENT

EmbracesEmbracesEmbracesEmbracesKNOWLEDGE SCIENCEKNOWLEDGE SCIENCEKNOWLEDGE SCIENCEKNOWLEDGE SCIENCE

The Intentional and Competitive Human EndeavorThe Intentional and Competitive Human EndeavorThe Intentional and Competitive Human EndeavorThe Intentional and Competitive Human EndeavorTo To To To Make Things Happen That Do Not Happen By ThemselvesMake Things Happen That Do Not Happen By ThemselvesMake Things Happen That Do Not Happen By ThemselvesMake Things Happen That Do Not Happen By Themselves

Testable Natural Sciences, Capable of Learning and Changing,Testable Natural Sciences, Capable of Learning and Changing,Testable Natural Sciences, Capable of Learning and Changing,Testable Natural Sciences, Capable of Learning and Changing,Pursuing Knowledge Advancements Toward Pursuing Knowledge Advancements Toward Pursuing Knowledge Advancements Toward Pursuing Knowledge Advancements Toward

Most Elegant Simplicity Most Elegant Simplicity Most Elegant Simplicity Most Elegant Simplicity and Ultimate Limit Efficiencyand Ultimate Limit Efficiencyand Ultimate Limit Efficiencyand Ultimate Limit Efficiency

Copyright Dr. Richard L. Ballard 2006KM Embraces KS.dsf

4

Most Revolutionary Most Revolutionary Most Revolutionary Most Revolutionary Time ImaginableTime ImaginableTime ImaginableTime Imaginable

Every organization can go out today and Buy a desktop computer with the capacity To store All The World's Knowledge.

TECHNICALLY and IN PRINCIPLE:

UNFORTUNATELY:

The institutions of human civilization have yet to: Decide what "knowledge is"? Assign unique identifiers to every idea? or Conclude what makes any idea truly Meaningful?

Still, Commercally, A Great Battle Has Begun

To Create Standard Answers to All Such Questions

Over The Next 5-7 years You Will Pick the Winner Copyright Dr. Richard L. Ballard 2006STK - Objectives 1.dsf

5

Questions About "Knowledge"Questions About "Knowledge"Questions About "Knowledge"Questions About "Knowledge"Are Always Deeper ThanAre Always Deeper ThanAre Always Deeper ThanAre Always Deeper Than

We Want To GoWe Want To GoWe Want To GoWe Want To Go

Coded Language of ThoughtExperimental Natural Sciences

Uncertain ProbabilityConceptualism

Semantic NetworksKnowledge Theory

Evolutionary Standards

Conventional LanguagesLogic & MathematicsCertain TruthObject OrientationRelational DatabasesInformation TheoryUnchanging Standards

EXPERIMENTAL / CONCEPTUAL /EXPERIMENTAL / CONCEPTUAL /EXPERIMENTAL / CONCEPTUAL /EXPERIMENTAL / CONCEPTUAL /DECLARATIVE StandardDECLARATIVE StandardDECLARATIVE StandardDECLARATIVE Standard

LOGICAL / LINGUISTIC /LOGICAL / LINGUISTIC /LOGICAL / LINGUISTIC /LOGICAL / LINGUISTIC /PROCEDURAL StandardPROCEDURAL StandardPROCEDURAL StandardPROCEDURAL Standard

Coptright Dr. Richard L. Ballard 2006STK - Objectives 2.dsf

CPU MACHINES

CPU & MEMORY MACHINES

Which One Can Really Do That?Which One Can Really Do That?Which One Can Really Do That?Which One Can Really Do That?

One Standard for Humans, One Standard for Humans, One Standard for Humans, One Standard for Humans, One for Machines, orOne for Machines, orOne for Machines, orOne for Machines, orOneOneOneOne Standard Integrating Standard Integrating Standard Integrating Standard Integrating ALL KNOWLEDGEALL KNOWLEDGEALL KNOWLEDGEALL KNOWLEDGE????

6



1 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010


1

101

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 9

1010

1011

1012

1013

1014


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Adapted from The Dragon's of Eden, Carl Sagan, 1977

Humans

Mammals

Reptiles

Jellyfish

Virus


Amphibians

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Genes vs Brains

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Instinctive DNA InheritanceInstinctive DNA InheritanceInstinctive DNA InheritanceInstinctive DNA Inheritance

constantly adaptingconstantly adaptingconstantly adaptingconstantly adapting""""brain contentbrain contentbrain contentbrain content ( ( ( (TheoryTheoryTheoryTheory))))""""with no need to changewith no need to changewith no need to changewith no need to change

their host's biological form their host's biological form their host's biological form their host's biological form

SENSE ORGAN SENSE ORGAN SENSE ORGAN SENSE ORGAN receipt ofreceipt ofreceipt ofreceipt ofInformationInformationInformationInformation produces produces produces produces

physiologicalphysiologicalphysiologicalphysiologicalsituation awarenesssituation awarenesssituation awarenesssituation awareness

-- with or without a brain.-- with or without a brain.-- with or without a brain.-- with or without a brain.

BRAINLESSBRAINLESSBRAINLESSBRAINLESS animals react animals react animals react animals reactonly from only from only from only from instinctive dnainstinctive dnainstinctive dnainstinctive dna

programsprogramsprogramsprograms -- to succeed or die. -- to succeed or die. -- to succeed or die. -- to succeed or die.poory adapted species die out. poory adapted species die out. poory adapted species die out. poory adapted species die out.

BRAINBRAINBRAINBRAIN memories memories memories memories model,model,model,model,store, and teachstore, and teachstore, and teachstore, and teach

successsful behaviorssuccesssful behaviorssuccesssful behaviorssuccesssful behaviorsas "lessons learned,"as "lessons learned,"as "lessons learned,"as "lessons learned,"

7

1

10 1

10 2

10 3

10 4

10 5

10 6

10 7

10 8

10 9

10 10

10 11

10 12

10 13

10 14

10 15

10 16

10 17

10 18

10 19

10 20

10 21

101 10181 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 10151016 1017

Human

Mammals

AmphibiansJellyfish

ProtozoaAlgae

Virus


Workgroups


Bacteria

Reptiles

Turn TowardTurn TowardTurn TowardTurn TowardAll Theories --All Theories --All Theories --All Theories --

All KnowledgeAll KnowledgeAll KnowledgeAll Knowledge

LogicLogicLogicLogic++++

InformationInformationInformationInformation

SOFTWARE

HARDWARE

DOS Windows 98

486 PC

PentiumLaptop

386 PC

KNOWLEDGE AGEKNOWLEDGE AGEKNOWLEDGE AGEKNOWLEDGE AGEREQUIREMENTSREQUIREMENTSREQUIREMENTSREQUIREMENTS



Intelligent Animals Embrace Many BehaviorIntelligent Animals Embrace Many BehaviorIntelligent Animals Embrace Many BehaviorIntelligent Animals Embrace Many BehaviorPatterns For Their Self-evident SuccessPatterns For Their Self-evident SuccessPatterns For Their Self-evident SuccessPatterns For Their Self-evident Success

Extremely Resource Aware,Extremely Resource Aware,Extremely Resource Aware,Extremely Resource Aware,Their Many Alternative GoalsTheir Many Alternative GoalsTheir Many Alternative GoalsTheir Many Alternative GoalsAre Are Are Are Intentional, Competitive,Intentional, Competitive,Intentional, Competitive,Intentional, Competitive,Success-Success-Success-Success-orientedorientedorientedoriented, and Often, and Often, and Often, and OftenAchievable in Achievable in Achievable in Achievable in Multiple Ways.Multiple Ways.Multiple Ways.Multiple Ways.

They reject options that They reject options that They reject options that They reject options that dodododonot match their situationnot match their situationnot match their situationnot match their situation or or or or

go against theories they trustgo against theories they trustgo against theories they trustgo against theories they trust....

They assume most choices areThey assume most choices areThey assume most choices areThey assume most choices arenot provably right or wrongnot provably right or wrongnot provably right or wrongnot provably right or wrong,,,,

seek to seek to seek to seek to enumerate all optionsenumerate all optionsenumerate all optionsenumerate all options,,,,and and and and predict the consequencespredict the consequencespredict the consequencespredict the consequences

of each option of each option of each option of each option before decidingbefore decidingbefore decidingbefore deciding....

Logical Self-consistencyLogical Self-consistencyLogical Self-consistencyLogical Self-consistencyinsures machine-like behaviorsinsures machine-like behaviorsinsures machine-like behaviorsinsures machine-like behaviors

guaranteed to follow externalguaranteed to follow externalguaranteed to follow externalguaranteed to follow externalmandates, e.g. common grammar mandates, e.g. common grammar mandates, e.g. common grammar mandates, e.g. common grammar Possess no intrinsic awarenessPossess no intrinsic awarenessPossess no intrinsic awarenessPossess no intrinsic awareness

of efficiency, resource usage, orof efficiency, resource usage, orof efficiency, resource usage, orof efficiency, resource usage, orthe complexitythe complexitythe complexitythe complexity of methodologies of methodologies of methodologies of methodologies-- a source of non-computability,-- a source of non-computability,-- a source of non-computability,-- a source of non-computability,when badly matched to problem.when badly matched to problem.when badly matched to problem.when badly matched to problem.


8

Albert Einstein -- Albert Einstein -- Albert Einstein -- Albert Einstein -- Ideas and OpinionsIdeas and OpinionsIdeas and OpinionsIdeas and Opinions, 1988, 1988, 1988, 1988Nobel Prize in Physics 1921Nobel Prize in Physics 1921Nobel Prize in Physics 1921Nobel Prize in Physics 1921

"As far as the propositions"As far as the propositions"As far as the propositions"As far as the propositionsof mathematics of mathematics of mathematics of mathematics refer to reality,refer to reality,refer to reality,refer to reality,

they are not certain;they are not certain;they are not certain;they are not certain;andandandand

As far as they are certain,As far as they are certain,As far as they are certain,As far as they are certain,they do not refer to reality."they do not refer to reality."they do not refer to reality."they do not refer to reality."

Einstein Physics Quote.dsf

Which Which Which Which truthtruthtruthtruth should our new science choose? should our new science choose? should our new science choose? should our new science choose?

So whileSo whileSo whileSo while

guaranteed self-consistency is the virtue of logic,guaranteed self-consistency is the virtue of logic,guaranteed self-consistency is the virtue of logic,guaranteed self-consistency is the virtue of logic,

guaranteed uncertainty is the accepted fact ofguaranteed uncertainty is the accepted fact ofguaranteed uncertainty is the accepted fact ofguaranteed uncertainty is the accepted fact ofexperimental quantum physics.experimental quantum physics.experimental quantum physics.experimental quantum physics.

Copyright Dr. Richard L. Ballard 2004

9

Ultimate InnovationsUltimate InnovationsUltimate InnovationsUltimate InnovationsThese are the inventions or discoveries that operate

at the Ultimate Natural Limits of Reality

To capture Every Form of Knowledge is not aboutdealing with infinite complexies, Nature is everywhere finite.

Successful representations of knowledge seek to findNature's Most Elegant Limit Simplicities.

ARGUABLY,These Leave No Room for Technical Improvement

Copyright Richard L. Ballard 2006Ultimate Innovations.dsf

10

Conceptualizing and OrganizingConceptualizing and OrganizingConceptualizing and OrganizingConceptualizing and OrganizingAll All All All of Imagination and Realityof Imagination and Realityof Imagination and Realityof Imagination and Reality

Abstraction"the act of consideringsomething as a general

quality or characteristic,apart from concrete

realities, specific objects,or actual instances"


Aggregation"collection of particularsinto a whole mass or sum"


Everything Real &Observable

EverythingImaginable

Every "Science" Every "Fantasy"

Copyright Richard L. Ballard 1998-2005Imagination & Reality.dsf

PracticalRationalism

ConceptsCategories

Epistemology

TheologyPure Intellect

Ontology

Imagination

Form

Beauty

TruthLogic

Axiomologies

Mathematics

Natural Laws

Requirements

Design

Faith

Mark 3 Top-MostPrimitives

(HyptheticalModels)

(Acquired Models)

Ideas Theories Models Formalisms


PerfectPerfectPerfectPerfectIntelligenceIntelligenceIntelligenceIntelligence

Ontological Primitives Cosmology

Chemistry

Biology


EnergyMatter

Machines

Planets

Animals

Galaxies

Local Groups

Societies

Particles


Sense DataPhenomena ObservablesEvents

Guanine

SKELETALDIGESTIVE

CIRCULATORYNERVOUS

11

ENTITY

N-ARYRELATIONSHIP

THEORY-BASED MEDIATING STRUCTURE

n=2 n>2

1

2

3

"Knowledge"Knowledge"Knowledge"KnowledgeTheory-basedTheory-basedTheory-basedTheory-based

Semantic Web"Semantic Web"Semantic Web"Semantic Web"


Metaphysics

TheologyCosmology

ChemistryBiology


Energy

Matter


Sense Data

Phenomena

Epistemology

Concepts

MaterialsMachines

LANGUAGE

ARTCategories

Exemplars

Realistic

Beauty

Truth

Pure Intellect

NaturalLanguages

Mathematics

Logic

Measurement

IconsPhotographs

SYMBOLS& CODES

Ontology






Physics


Abstract





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P(a,

b, c

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P(a,

b, c

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| ...

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P( ..

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×

SITU

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Knowledge TheoreticKnowledge TheoreticKnowledge TheoreticKnowledge TheoreticRepresentations of ThoughtRepresentations of ThoughtRepresentations of ThoughtRepresentations of Thought

Knowledge Theoretic Representation.dsf

Models

Situation ConstrainedNavigation of Every

Accepted Fact, Theory, &Predictable Decision Impact

12Model Instance Codes.dsf Copyright Richard L. Ballard 2003



PhysicsMetaphysics

Model

Inst

a 1 - Nnces

Instance 0PhysicalConcept

Inst

a 1 - Nnces

ModelInstance 0

Meta-PhysicalConcept




IntelligenceIntelligenceIntelligenceIntelligence

DataTypeModel

Inst

a 1 - Nnces

Instance 0

DataFormConcept


of Knowledge

Model Model Model Model - - - - InstanceInstanceInstanceInstance Concept Codes Concept Codes Concept Codes Concept Codes Are Unique and Identical Are Unique and Identical Are Unique and Identical Are Unique and Identical In All Languages In All Languages In All Languages In All Languages




A CONCEPT (model-instance) appearsonly once in any semantic web, its unique code locates it instantly -- without search

13

1970-1993 1970-1993 1970-1993 1970-1993 NSF Science EducationNSF Science EducationNSF Science EducationNSF Science EducationQuantum Simulations &Quantum Simulations &Quantum Simulations &Quantum Simulations &

"Star Wars" "Star Wars" "Star Wars" "Star Wars" Battle ManagementBattle ManagementBattle ManagementBattle Management

Produce Ultimate Limit Theory For Knowledge and Computation

Ballard (1987-1993)

Defines First DeclarativeDefines First DeclarativeDefines First DeclarativeDefines First DeclarativeTheory-Based Semantic WebTheory-Based Semantic WebTheory-Based Semantic WebTheory-Based Semantic Web

Predicting All Decison ImpactsPredicting All Decison ImpactsPredicting All Decison ImpactsPredicting All Decison ImpactsBefore Choices Are MadeBefore Choices Are MadeBefore Choices Are MadeBefore Choices Are Made

Copyright Knowledge Foundations Inc. 2006First Semantic Web.dsf

14

Natural Science Theory-basedNatural Science Theory-basedNatural Science Theory-basedNatural Science Theory-based THEORY OF KNOWLEDGETHEORY OF KNOWLEDGETHEORY OF KNOWLEDGETHEORY OF KNOWLEDGE

AND COMPUTATION AND COMPUTATION AND COMPUTATION AND COMPUTATION

1993 - 2006BREAKTHROUGH IMPORTANCE BREAKTHROUGH IMPORTANCE BREAKTHROUGH IMPORTANCE BREAKTHROUGH IMPORTANCE

of Possessing Only Comprehensive

Brain-basedIntelligence Is Empirically Predictive,-- Not Merely Categorically Reactive

Ontological Object Orientation andLogical Inference Simply React

Most Essential Breakthrough.dsf Copyright Knowledge Foundations 2006

15

X



GoalsGoalsGoalsGoalsEducationEducationEducationEducationResponsibilityResponsibilityResponsibilityResponsibilityRequirementsRequirementsRequirementsRequirementsIntentIntentIntentIntent

TimeTimeTimeTimeRelationRelationRelationRelation

ResourceResourceResourceResourceOpportunityOpportunityOpportunityOpportunity

ActionActionActionAction





Probability ofProbability ofProbability ofProbability ofKnowing EveryKnowing EveryKnowing EveryKnowing Every

Option OutcomeOption OutcomeOption OutcomeOption OutcomeBefore DecisionsBefore DecisionsBefore DecisionsBefore Decisions

Are MadeAre MadeAre MadeAre Made



Physical EventPhysical EventPhysical EventPhysical Event of of of of"Thought""Thought""Thought""Thought" or or or or"Execution""Execution""Execution""Execution"


RealityRealityRealityRealitya' prioria' prioria' prioria' priori a' posterioria' posterioria' posterioria' posterioriDegrees of Freedom & Degrees of Freedom & Degrees of Freedom & Degrees of Freedom & ConstraintConstraintConstraintConstraint



a, b, c, ... ... x, y, z




16

Congress


AlliedGovernments


Political &Economic

Base


DoDOrganization



MilitaryFacilities

NavyOrganization



MajorSubcontractors

ThreatGeography

OperationalForces

MissionRequirements

ProcurmentPrograms

Cost &Technology

RiskModeling

ThreatScenarios

OperationalConcepts

R&DPrograms

Major WeaponSystems


ThreatDatabases

WarfareRequirements



MajorSubsystems


DesignModel Base

TechnologyTestbeds


SystemTest

Requirements

TrainingObjectives


MissionSimulation

SystemSimulation

SystemTest

Facilities


TrainingFacilities


PerformanceAnalysis

Model Base

1

2

3

Sit uationSit uationSit uationSit uation

4

LocatingLocatingLocatingLocating

5

6

Tar getTar getTar getTar get

7

Wha t is There?Wha t is There?Wha t is There?Wha t is There?

8

Tak es ou tTak es ou tTak es ou tTak es ou tJ-5 tooJ-5 tooJ-5 tooJ-5 too

Who has got one?Who has got one?Who has got one?Who has got one?

20 MatchingMatchingMatchingMatchingNAT O NAT O NAT O NAT O JammerJammerJammerJammer

BandBandBandBand19

Just the Just the Just the Just the Bad Gu ysBad Gu ysBad Gu ysBad Gu ys

18WhoWhoWhoWhoelse?else?else?else?

17Wha tWha tWha tWha tfrequ enc ies?frequ enc ies?frequ enc ies?frequ enc ies?

1615

Rad ar?Rad ar?Rad ar?Rad ar? 15Armored,Armored,Armored,Armored,

Shoots bigger ,Shoots bigger ,Shoots bigger ,Shoots bigger ,farther farther farther farther & & & &

faster than faster than faster than faster than usususus

Top Gu nTop Gu nTop Gu nTop Gu n

Can he see us?Can he see us?Can he see us?Can he see us?

14 13Hea rt ofHea rt ofHea rt ofHea rt ofDa rknessDa rknessDa rknessDa rkness

12Where is hisWhere is hisWhere is hisWhere is his

weak ne ss?weak ne ss?weak ne ss?weak ne ss?

Or ga nizationOr ga nizationOr ga nizationOr ga nization& Equipment& Equipment& Equipment& Equipment

11Nightmar e, WhenNightmar e, WhenNightmar e, WhenNightmar e, WhenFlying low!Flying low!Flying low!Flying low!

10Who is shootingWho is shootingWho is shootingWho is shooting

at Us?at Us?at Us?at Us?

9

Who c arriesWho c arriesWho c arriesWho c arriesthis system?this system?this system?this system?

22 Wha t elseWha t elseWha t elseWha t elsedo you ca rry?do you ca rry?do you ca rry?do you ca rry?23 I wa nt this guyI wa nt this guyI wa nt this guyI wa nt this guy

with u s!with u s!with u s!with u s!

Mou nt theMou nt theMou nt theMou nt theband 10band 10band 10band 10

jammer pod!jammer pod!jammer pod!jammer pod!

24



Countries



Facilities



Components


Components


Model: COUNTRIES



Model:AAA BATTERIES




Rel:JAM/FREQ

7

3

2

3

4 8


Air Defenses

149 25 2


Equipment


Equipment


Equipment


Model: AAA PLATOONS





5


Rel:SOURCE/FREQ

42


Model:CHANNELS

35 8

Instance 3:Jam/Freq


Model:JAM BAND

Instance 1: ALQ-99F

Air For ceAir For ceAir For ceAir For ce Na vyNa vyNa vyNa vy21

Rel:SYS/BAND

Instance 5:Pod/Freq


Model:JAMMER SYSTEM

55 21 2


Model:EA-6B WEAPONS


Model:EA-6B Prowler


8

3 4

2

1

756

9

12

10 11

13

15 16 17

2122

2324

18

1920

14


1990-2005


1990-2001


PREDICTIVE WEB P(a, b, c ,... | ... x, y, z)



CONOPSOption #1

N = 4DECISIONS:

Predicting and AssessingPredicting and AssessingPredicting and AssessingPredicting and AssessingDecision ImpactsDecision ImpactsDecision ImpactsDecision Impacts

THEORYN = 7

Decisivedegrees

offreedom

Copyright Knowledge Foundations 2006Semantic Web.dsf

17


Semantic Semantic Semantic Semantic WebWebWebWeb RealityRealityRealityReality SITUATION PREDICTIVE WEB DECISION IMPACT

P(a, b, c ,... x, y, z) P(a, b, c ,... | ... x, y, z) P( ... x, y, z)==== ××××Fundamental Ultimate Limit MeasuresFundamental Ultimate Limit MeasuresFundamental Ultimate Limit MeasuresFundamental Ultimate Limit Measures




Knowledge Theory-basedKnowledge Theory-basedKnowledge Theory-basedKnowledge Theory-basedUltimate MinimumUltimate MinimumUltimate MinimumUltimate Minimum

Decision Resource CostDecision Resource CostDecision Resource CostDecision Resource Cost

-log{P(a, b, c,...x, y, z)}

ShannonShannonShannonShannonInformation BandwidthInformation BandwidthInformation BandwidthInformation Bandwidth

& Storage Limit Cost& Storage Limit Cost& Storage Limit Cost& Storage Limit Cost

-log{P(...x, y, z)}

BallardBallardBallardBallard Education, Web Certification, Education, Web Certification, Education, Web Certification, Education, Web Certification,

& Theory Capture Limit Cost& Theory Capture Limit Cost& Theory Capture Limit Cost& Theory Capture Limit Cost

-log{P(a, b, c,...|....x, y, z)}

BallardBallardBallardBallard Education, Web Certification, Education, Web Certification, Education, Web Certification, Education, Web Certification,

& Theory Capture Limit Cost& Theory Capture Limit Cost& Theory Capture Limit Cost& Theory Capture Limit Cost

-log{P(a, b, c,...|....x, y, z)}Theory predicts thatcosts can & will scale

proportionally toInformation Content

Theory provides performance-based measures comparing

Education, Theory Capture, &Knowledge Creation investment

Theory links task specificsuccesses to most effectivetrade-offs in training, theorycreation, & technology use




a' prioria' prioria' prioria' priori a, b, c, ... ... x, y, za' posterioria' posterioria' posterioria' posterioriDecision Success P(task)Decision Success P(task)Decision Success P(task)Decision Success P(task)

18

Knowledge is defined by a question or set of questions for which the answers are initially in doubt

Axiom I.Axiom I.Axiom I.Axiom I.

What outputs will this black box produce? 0000 0000 00000

1

2

34

5

6

7

Quantitative measure of knowledge requires specification of the acceptable answer forms.

Axiom II.Axiom II.Axiom II.Axiom II.

Display capable of 0 - 255 0000 0000 0000 2222 5555 5555

The number of such forms is the initial measure of Answer Uncertainty -- i.e. Problem Size.

Axiom III.Axiom III.Axiom III.Axiom III.

256 = 28 Possible Answers = 8 bits of UncertaintyProblemSize =

Axiom IV.Axiom IV.Axiom IV.Axiom IV. Knowledge is the possession or receipt of anything -- fact or theory -- that reduces answer uncertainty.

Knowledge theory applies equally to any information, theory,rule, or experience that constrains answer choice

Axiom V.Axiom V.Axiom V.Axiom V. Knowledge is measured by the amount uncertainty is reduced

Theory 1: Answer is always EvenTheory 1: Answer is always EvenTheory 1: Answer is always EvenTheory 1: Answer is always Even

Theory 2: Answer is multiple of 4Theory 2: Answer is multiple of 4Theory 2: Answer is multiple of 4Theory 2: Answer is multiple of 4

Theory 3: Answer y = 4 xTheory 3: Answer y = 4 xTheory 3: Answer y = 4 xTheory 3: Answer y = 4 x2222 or or or or blank blank blank blank

0000 0000 00000

1

2

34

5

6

7

yyyyxxxx

Judging knowledge content THEORYyyyyxxxx

EXPERIMENT

0 01 42 163 364 645 1006 1447 196

28 => 27 possible answers i.e. 1 bit Theory



Information ContentInformation ContentInformation ContentInformation Content8 = 23 answers, i.e. 3 bit Info

8 bits uncertainty-5 bits theory-3 bits information 0 bits answer uncertaintyAnswer is always predicted

Sample Application of Knowledge MeasuresSample Application of Knowledge MeasuresSample Application of Knowledge MeasuresSample Application of Knowledge Measures

Knowledge ConstrainsKnowledge ConstrainsKnowledge ConstrainsKnowledge ConstrainsAnswer ChoicesAnswer ChoicesAnswer ChoicesAnswer Choices

AcceptedAcceptedAcceptedAccepted THEORIESTHEORIESTHEORIESTHEORIESConstrain UsConstrain UsConstrain UsConstrain Us Absolutely Absolutely Absolutely Absolutely

orororor Conditionally Conditionally Conditionally Conditionally

INFORMATIONINFORMATIONINFORMATIONINFORMATION SelectsSelectsSelectsSelectsAmong TheoreticallyAmong TheoreticallyAmong TheoreticallyAmong TheoreticallyAcceptable AnswersAcceptable AnswersAcceptable AnswersAcceptable Answers

Those that Best FitThose that Best FitThose that Best FitThose that Best Fitour our our our Current SituationCurrent SituationCurrent SituationCurrent Situation

Humans willinglyHumans willinglyHumans willinglyHumans willinglyconsider Any choiceconsider Any choiceconsider Any choiceconsider Any choice

not viewed asnot viewed asnot viewed asnot viewed asprovably false, provably false, provably false, provably false,

dangerous,dangerous,dangerous,dangerous,or prohibitivelyor prohibitivelyor prohibitivelyor prohibitively

ExpensiveExpensiveExpensiveExpensiveCopyright Richard L. Ballard 1998-2006

In General ....In General ....In General ....In General ....

Axiomatic Definition of Knowledge &Human-like Constraint-based Reasoning

Axiomatic Knowledge Measure.dsf

19

Mark 3 Memory ArchitectureMark 3 Memory ArchitectureMark 3 Memory ArchitectureMark 3 Memory Architecture"Ockham's Razor""Ockham's Razor""Ockham's Razor""Ockham's Razor" Memory Machines Memory Machines Memory Machines Memory Machines

""""No repetition beyond necessity"No repetition beyond necessity"No repetition beyond necessity"No repetition beyond necessity" -- Will iam of Ockham,-- Will iam of Ockham,-- Will iam of Ockham,-- Will iam of Ockham,14th Cent ury Eng lis h Sch olastic Phi l osopher

Ballard's 2003 Scalabil ity ConjectureBallard's 2003 Scalabil ity ConjectureBallard's 2003 Scalabil ity ConjectureBallard's 2003 Scalabil ity Conjecture -- -- -- -- Non-redundant, variable length concept codingNon-redundant, variable length concept codingNon-redundant, variable length concept codingNon-redundant, variable length concept coding appears to be a appears to be a appears to be a appears to be a necessary condition fornecessary condition fornecessary condition fornecessary condition for "linear" (proportional) Storage Scalability"linear" (proportional) Storage Scalability"linear" (proportional) Storage Scalability"linear" (proportional) Storage Scalability

ConclusionsConclusionsConclusionsConclusions -- We can simulate future computing -- We can simulate future computing -- We can simulate future computing -- We can simulate future computing architectures transparently architectures transparently architectures transparently architectures transparently within existingwithin existingwithin existingwithin existing CPU machines and software operating systems. CPU machines and software operating systems. CPU machines and software operating systems. CPU machines and software operating systems.

Stil l we Stil l we Stil l we Stil l we expect and plan to build "brain-like", expect and plan to build "brain-like", expect and plan to build "brain-like", expect and plan to build "brain-like", memory-centered declarative processing memory-centered declarative processing memory-centered declarative processing memory-centered declarative processing chipschipschipschips within the next 5 years. within the next 5 years. within the next 5 years. within the next 5 years.

It appears likely, that the cyclic, repetitious,It appears likely, that the cyclic, repetitious,It appears likely, that the cyclic, repetitious,It appears likely, that the cyclic, repetitious, processing of fixed-sized word and record processing of fixed-sized word and record processing of fixed-sized word and record processing of fixed-sized word and record structures may be structures may be structures may be structures may be precisely the wrong designprecisely the wrong designprecisely the wrong designprecisely the wrong design to achieve knowledge-theoretic limit behaviorsto achieve knowledge-theoretic limit behaviorsto achieve knowledge-theoretic limit behaviorsto achieve knowledge-theoretic limit behaviors. . . .

Copyright Richard L. Ballard 2006Ockham's Razor.dsf

20

Knowledge ResearchHuntington Beach, California, 92647

714-842-8091

Science & TechnologyRequirements Guidance

(STRG) June 1996KRC Doc # 50

PerformanceMetric (MOP)

Required

12 Functional Areas2-10 Sub-Areas/Area

SupportPriority

WarfarePriorityH - High

M - MediumL - Low

System/CapabilityImproved

Fleet CINCCommandTechnologyIssues (CTI)

ParticipantOrganizations

Critical Issues &Show Stoppers

Functional AreaDefinitions

SpecificRequirements

Sub-AreaDefinitions

GeneralRequirements

Func Sub-Area

General Req

Priority

Req Priority

Spec Improvement

Func Sub-AreaImplied System

Capability

Performance Req

Gen Improvement

Fleet

Cmd Tech Issues

Number of Issues

CTI Inputs

FleetParticipants

FleetPriorit ies

BudgetCategories

6.2 6.3

DTAP &DTO Funding


10 plans


TechnologySub-Area

Plans



Sub-AreaDevelopment

Efforts

ProgramElements


IndustryPrograms

GovernmentPrograms

ProgrammedPE Effort


DeficiencyScenario

AssumedBaselines


Performance (MOP)

ChallengeStatements

1995200020052010

Time Periods

TechnologyAreas

10 Areas

Tech Sub-Area

Time Period

Baseline

Transition Goals

Tech Area

Budget


FY 1997

Baseline

Pay-off Objective


Attribute Set

Adv TechDemos

(ATD/ACTD)


Sub-Area Roadmap

Tech Sub-Area

Fiscal Year

Develop. Effort

JWSTP & DTAPDTO Funding

1997

BudgetCategories

6.2 6.3

4-15 / objective

12 objectives

4-48 / objective

CapabilityElements

4 concepts

Vision 2010OperationalConcepts

10 op areas

JWCAOperational AreasJoint Warfighting

Science &TechnologyArea Plan (JWSTP)

April 1996KRC S&T KB Doc # 38

J o in tW a r f ig h t i n gE x p e r im e n t

( J W E )

A dv C once ptT echD em os

(A C TD )

A d v T e c hD e m o s(A T D )

D e fens eT ech no logyO b j (D T O )

P lan ne dA ct ivi ty

FunctionalCapabilities

DemonstrationTitle

LimitationStatement

P roduct Techno logy

Capability Element

Element Goal

Funct Capability

Functional Goals

Capability Element

Func Capability

Level of Support

Functions Needed

ElementGoal

DTAP DTOs

FY 1997

JWCO

Budget

JWSTP DTOs

Level of Support

S t r o n gM o d e r -

a t e

2010 Op Concepts

JWCO

Level of SupportVisi on 2010Sup po rt

Joint WarfightingCapabilityObjectives(JWCO)

Capability Element

Element Goal

Limitation

Element Lim ita tion

JWCA Op Areas

JWCO

Level of Relation

JWCA / JWCO

Fiscal Year

JWCO

Planned Activity

JWCO Roadmap

Capability Element

Element Goal

Product / Tech

Key Technologies

Level of RelationS t r o n gM o d e r -

a t e

Concept1 Title

Concept2 Title

Concept3 Title

Agency Support

Demo Title

Capability Element

Level of Support

Demo SupportFY96 Air Materiel CommandTechnology Area PlansAerospace, Air Vehicles,

Avionics, C3I, & Weapons

ONR 35 DatabaseSearch Reports

Selected AviationProgram Element

Descriptions

BudgetCategories

6.1 6.2 6.3 KRC Doc # 8 & 9

Naval AviationScience & Technology

Program Sept 1995

5 product lines

Joint Mission/Support Areas

(JMA/SA)S&T PriorityCategories

9 categoriesFocusAreas

Req. ProductCapabilityPerformance

Product Lines

TechnologySubdivision

Phone

Point ofContact

Challenges TypicalProducts

EmphasisArea

AddressesApplies toRelated to

RequirementApplicability

Recent TechTransitions

Funded Projects

Naval AviationS & T Program

Aviation-relatedTech Area Plans

(DTAP)

DTAP

Nava l AviationS&T Funds

Non-NavyManTech

SBIR

RevolutionaryMission

Capabil itiesAcceleratedCapabil ity

Initiatives (ACI)

Current/FuturePlatforms

MajorAccomplishments

6.1 Funds

Product Line

S&T Funds

Budget $

FY 94-96

Fiscal Year

Emphasis AreaRequired

Product/Capability

Potent. Product

Tech Subdivision

Contact

Organization

Cross Links

Far-term GoalMid-Term Goal

Tech Subdivision

Product

Performance Goal

Near-Term GoalGoalsNeeds

6.1 6.2 6.3

EngineeringPhase

Priority Category

Focus Area

Product Line

PLT Response

Priority Category

Funded Project

Req Applicability

Project vs Req

Priority Category

Focus AreaRequired

Product/Capability

S&T Priorities

Fiscal Year

PlatformRequired

Product / Capability

Transition Ops

Fiscal year

Platform

Engineering Phase

Transition Window

RevolutionaryCapability

JW Missions

CinC/OPNAV Req

S&T Guidance

Tech Subdivision

Funds

Budget $

FY 95

Battlespace Management

THRUST 1: AIR COMBATSITUATION AWARENESS

Cockpit Displays:Information Fusion3-D Out-of -cockpit Tactical Scene Rendering2-D Mission Mapping

Open Processor Architecture

Fibre Channel InterfacePowerscene IntegrationCOTS Additions to Tactical Data Systems

Sh ared AperaturesLow Band SERAT

High Band ASAP

Affordability

Antenna Arrays

Structurally Embedd edFabricationMaintenance

Standard Modules

Digital Signal Proces sing

Affo rdable/Low Pow er

All RF S ignals

TASK 1.6 ADVANCEDCOMMON ELECTRONIC

MODULES

Optical Network

Extremely High Data RatesInfinite Growth

Smart Cockpit/Crew station Proj ect (SCCP)

Helmet Systems:

Image Processing for Helmetor Panel Display

Stat e-of- the A rt C om mer cial Pro ces sorsParallel S ignal ProcessingCommercial G raphics Engines

Avionics CoolingHigh Thermal Densit iesStandard Packaging

JSF 2000-2040Avionic Architectures

VehicleManagement

System

Stores Management System

Integrated EO Sensing

Integrated RF S ensingSharedApertures

IntegratedCor ePr ocessing

Pilot VehicleInterfacing

Mod ul eMec han ical& Cooli ng

UNIFIED DIGITALAVIONICS NETWORK

Weapon SystemInterface

TASK 1.3 REAL-TIME AIRBO RNEHIGH DEFINITION IMAGE

PROCESSI NG & SELECTION

MAST 1 IMPROVED SITUATION AWARENESS,TARGETING, AND MISSION MANAGEMENT

Aircrew Decis ion A iding Interf ace (ADAI )Smart Es cape Sys tem Analyz er (SESA)

Aircrew M iss ion Support Sys tem Analy zer(AMSSA)

Selected Air Combat VehicleFY97 Thrust 1 Task Documents

TASK 1.4 3-D VOLUMETRIC DISPLAY

HUMAN FACTORS METRICS

TASK 1.1 ADVANCED COCKPIT TECHNO LO GY

MAST 2 SCALABLE OPEN ARCHITECTURE

MAST 5 ADVANCED RFTECHNOLOGY

SSA SMART SKINSARRAY ATD

MAST 4 AVIONICS INTERCONNECTS

MAST 6 ADVANCEDPACKAGING

FLAT PANEL HELET MOUNTED DI SPLAY

TASK 1.2 VISUALLY COUPLED DISPLAYSYSTEMS TECHNOLOGY (VCDST)

TASK 1.5 VEHICLE MANAGEMENT SYSTEM

MAST 3 ADVANCED PROCESSOR

Advanced Helmet Vision Syste m (AHVS)Crusader Cyborg EyeJoint Helmet Mount ed Cueing System (JHMCS)Real-Time Ret argeting Helmet Mount ed DisplaysNavy Standard Magnetic Tracker (NSMT)

Processing Algorith msImage Indexing & SelectionSensor ImagesOn/Off-board ImagesArchived Images

Dr. R.L. Ballard & M. NawrockiStrike Warfare Analysis 9/96

RESEARCH

BASE

I N D U S T R I A L B A S E

OperationsAdvisoryGroups

Concept ofOperations(CONOPS)

MissionTaskPlans

MissionProfiles

NationalObjectives

SecurityObjectives

MilitaryObjectives

CampaignObjectives

OperationalObjectives

OperationalTasks

KeyAttribute

Requirements

STRATEGYTO TASK

Roadmaps

Platform

Product

Roadmaps

Visions

&

Architectures

Strike WarfareDeficiencies

Mission Needs

OperationalRequirements

FunctionalRequirements

PerformanceRequirements

Defense Appropriations6.1 6.2 6.3 6.4 6.5

Joint RequirementsOversight Council (JROC)

Joint WarfightingCapability

Assessment (JWCA)

White HouseNational Security

S&T Council (NSTC)

ChairmanJoint Chiefs of

Staff (JCS)


(OSD)

DirectorDefense Research &Engineering (DDRE)Defense Science &

Technology Reliance

TOB (CRP)Memorandum

Sept 1994

Chief of NavalResearch (CNR)

Naval AviationProduct LineTeams (PLT)

TransitionOpportunitiesBoard (TOB)

Program ManagersAir (PMAs)

Investment

Pillars

Joint Vision2010

Technology AreaPanels

Defense Science &Technology Advisory

Group (DSTAG)DefenseS&T Strategy

National SecurityS&T Strategy

Program Planning& Budget System

Chief of NavalOperations (CNO)

Fleet CINCHeadquarters

Fleet CommandTechnologyIssues (CTI)

DirectorNavy T&E

andTechnologyReq (N091)

S&T Round-tableProcess

Oct 1994Round Table

Future YearDefense Plan

Office of theSecretary of the Navy

(OSN)

Naval AviationSystems TEAM

(NAVAIR)

Aviation ProgramExecutive Officers

(PEO)

4.0 Research &EngineeringCompetency

Naval AviationScience & Technology

Office (NAVSTO)

Office of NavalResearch(ONR-03)

Basic ResearchPlan (BRP)

ProductProgramElements

ProgramManagerAviation

Warfare AreasStrike Warfare

PlatformProgramElements

ProgramManagerAviation

Congress President

DefenseTechnology

Objective (DTO)

Defense TechnologyObjectives (DTO)

10 panels

Deputy DDR&EReliance Executive

Committee (EXCOM)

Asst Sec of NavyResearch, Development,

& Acquisitions(ASN-RD&A)

RATIONAL BASELINE ANALYSISof Science & Technology

Source Documents

Research, Development, & Procurement

WARFIGHTERSAssessment of gaps in DoDScience & Technology sourcedocuments. Led to documentredesigns & new knowledgebase initiatives. ONR 1997

National Strategies for Meeting Technology ChallengeScaling Knowledge Management To Unlimited SizeScaling Knowledge Management To Unlimited SizeScaling Knowledge Management To Unlimited SizeScaling Knowledge Management To Unlimited Size

Scaling to Unlimited Size.dsf Copyright Richard L. Ballard 1994-2006

21Copyright Richard L. Ballard 2002-2005

N5 10 15

~N2

~N

ExplosiveExplosiveExplosiveExplosiveComplexity GrowthComplexity GrowthComplexity GrowthComplexity Growth

Costs Are Linear In

Decisive Choice Options

ImpossibleImpossibleImpossibleImpossibleExecution orExecution orExecution orExecution or

Database Database Database Database Costs Costs Costs Costs

THEORYTHEORYTHEORYTHEORY as an N-ary Bundleas an N-ary Bundleas an N-ary Bundleas an N-ary Bundle

Time PeriodTechno Sub-Area

Attribute SetBaseline Capability

Improve Sought (%)

Pay-off Objective

N = 5 N = 5 N = 5 N = 5 DecisionDecisionDecisionDecisionOptionsOptionsOptionsOptionsCompetitiveCompetitiveCompetitiveCompetitive Strategies Strategies Strategies Strategies

PossiblePossiblePossiblePossible


DeficiencyScenario

AssumedBaselines

Transition Goals

Baseline

Pay-off Objective


Attribute Set


N=5N=5N=5N=5

Tech Sub-Area

Time Period

Baseline

BudgetCategories

6.2 6.3

DTAP &DTO Funding


10 plans


TechnologySub-Area

Plans



Sub-AreaDevelopment

Efforts

ProgramElements


IndustryPrograms

GovernmentPrograms

ProgrammedPE Effort


Performance (MOP)

ChallengeStatements

1995200020052010

Time Periods

TechnologyAreas

10 Areas

Tech Area

Budget


FY 1997

Adv TechDemos

(ATD/ACTD)


Sub-Area Roadmap

Tech Sub-Area

Fiscal Year

Develop. Effort

"q" "r" "s" "t" "u"

"v"

"w"

"x"

"y""z"

"q" "r" "s" "t" "u"

"v"

"w"

"x"

"y""z"

KNOWLEDGE SUPERIORITYKNOWLEDGE SUPERIORITYKNOWLEDGE SUPERIORITYKNOWLEDGE SUPERIORITYTrade-offs in Minutes, Trade-offs in Minutes, Trade-offs in Minutes, Trade-offs in Minutes, not Monthsnot Monthsnot Monthsnot Months

Knowledge Acquisitionby Modeling

Whole DocumentsOriginal Document Creation Cost

$2-10 Million / originalDirect Modeling & Replace Cost

$5-7 Thousand / original

"KILLER APP" OBJECTIVECapture Original Content Directly

30-90% Creation Savings

All Knowledge Costs Scale ProportionallyAll Knowledge Costs Scale ProportionallyAll Knowledge Costs Scale ProportionallyAll Knowledge Costs Scale Proportionally

All Knowledge Costs Proportional.dsf

22

self-organizing self-organizing self-organizing self-organizing machines & theoriesmachines & theoriesmachines & theoriesmachines & theories

SBIR Phase 1 1997, Phase 2 1998, Phase 3 1999

Knowledge-based ComputingKnowledge-based ComputingKnowledge-based ComputingKnowledge-based Computing

Simulating Strike Fighter Aircraft'sSimulating Strike Fighter Aircraft'sSimulating Strike Fighter Aircraft'sSimulating Strike Fighter Aircraft'sMulti-computer Avionics NetworksMulti-computer Avionics NetworksMulti-computer Avionics NetworksMulti-computer Avionics Networks

F/A-18 E/F & Joint Strike Fighter

F-14 A/B/D Lantrin Laser DesignatiorJAST Master Plan Requirements

Windows NT Network ofWindows NT Network ofWindows NT Network ofWindows NT Network ofSimulated Avionics ModulesSimulated Avionics ModulesSimulated Avionics ModulesSimulated Avionics Modules

JF III Pilot-in-the-LoopJF III Pilot-in-the-LoopJF III Pilot-in-the-LoopJF III Pilot-in-the-LoopFlight Simulator RealityFlight Simulator RealityFlight Simulator RealityFlight Simulator Reality

IPX NetworkMessaging Loop

Playback Version

JFIII Real-Time Out-of-CockpitVisual Display

MissionPlayback

Data Store

Maps &Mission

Scenarios

ScenarioEditor

Pilot HOTASStick, Throttle, &

Switches

Infra-Red Sensor & Out-of-Cockpit

Displays

IPX Slave TargetImaging & Scene

Rendering Modules

Backseater HOTASStick, Throttle, &

Switches

FOUNDATIONSMark 2

Knowledge Base& "Virtual

Database Server"

AvionicsPrototyping Tool

Module Scheduler

IPXListenerModule

TimeSyncTalkerModule

Distributed Common Object Model(DCOM) Network

HDD Map Module

Up Front ControllerModule

MapTranslation

Program

Simulator Test Flights Feb 1998Simulator Test Flights Feb 1998Simulator Test Flights Feb 1998Simulator Test Flights Feb 19986 Naval & Marine Test Pilots

Patuxent River NASNaval Test & Evaluation Squadron 1

Uses one scaleable knowledgetool to acquire, integrate,

and emulate whole networkof non-scalable databases

F/A-18 & F-14F/A-18 & F-14F/A-18 & F-14F/A-18 & F-14Strike Warfare Strike Warfare Strike Warfare Strike Warfare

Testbed & TrainerTestbed & TrainerTestbed & TrainerTestbed & Trainer

AVIONICS PROTOTYPING TOOLAVIONICS PROTOTYPING TOOLAVIONICS PROTOTYPING TOOLAVIONICS PROTOTYPING TOOL

Copyright Knowledge Foundations 1996-2006

23

Standard Software Module AssumptionsStandard Software Module AssumptionsStandard Software Module AssumptionsStandard Software Module Assumptions

Any SoftwareModule

Any InputRelational

DB SchemaAny OutputRelational

DB Schema

ASCII Outlineof Database

ModelASCII Outlineof Database

Model

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULE

InputData

Record

OutputData

Record

AVIONICSMODULESIMULATION

SCHEDULER

One Knowledge Base Server One Knowledge Base Server One Knowledge Base Server One Knowledge Base Server IntegratesIntegratesIntegratesIntegrates All Schema & Emulates All Schema & Emulates All Schema & Emulates All Schema & Emulates Exactly Each Exactly Each Exactly Each Exactly Each Module's Expected Database BehaviorsModule's Expected Database BehaviorsModule's Expected Database BehaviorsModule's Expected Database Behaviors

Each Avionics SystemsManufacturer Develops

Their Own Simulation ModuleWorking Between Any Input& Ouput Database Schemas

1997-1999Avionics Prototyping Tool (APT) VirtualDatabase & Flight Simulation SBIR Phase IISPONSOR: Naval Air Systems Command, Pax River

TEAM-MATES:Knowledge Research

Knowledge FoundationsPacific-Sierra Research

& Mission Studio

APT Database Integration.dsf

Any SoftwareModule

Any OutputRelationalDatabase

Any InputRelationalDatabase

Any SoftwareModule

Any OutputRelationalDatabase

Any SoftwareModule

Any OutputRelationalDatabaseAny Input

RelationalDatabase

Any SoftwareModule


RelationalDatabase Any Software

Module

Any OutputRelationalDatabaseAny InputRelationalDatabase

Any SoftwareModule


RelationalDatabase

Any SoftwareModule


RelationalDatabase

OneOneOneOneKnowledgeKnowledgeKnowledgeKnowledge

BaseBaseBaseBase

Any InputRelationalDatabase

Copyright Knowledge Research 1997-2006

24

SBIR Phase 1A Delivered May 1994

Building TPIPTRoadmaps

AFSOCTechnologyRoadmap

Knowledge Base

Carroll's Government& Defense

OrganizationKnowledge Base

Product PrototypeDelivered Dec 1994

Technology Capture& IntegrationInitial Designs

to AIR-531

Aircrew Systems forPrecision Strike

Technology IntegrationKnowledge Base

Demonstrated &Delivered May 1995

Phase I Warfighting &Systems Technology

RequirementsJoint Advanced

Strike Technology(JAST)

Knowledge Base

Cataloging and integrating simulators,models, data sources

Delivered January 1994 Joint ServiceUniversal Threat

Simulation SystemKnowledge/Model Base

Cataloging CurrentFacilities and Capabilities

Delivered January 1993 NAWCJoint Service

T&E CapabilitiesKnowledge Base

Technology &Economic Impact

Delivered June 1992 NASA SpaceExplorationsTechnology

Concept Demo

Budget planning andwarfare assessment

Delivered June 1991 OPNAV FastPlanSummary Warfare

AppraisalKnowledge Base

$350K

$30K

$150K

$350K

$250K

$200K

$85K

$85K

$1.5 Million labor + $350K licenses

AGGREGATE AEROSPACEKNOWLEDGE ACQUISITION

PROJECTS 1991-1996

KNOWLEDGE RESEARCH


1990-2005


1990-2001

Congress


AlliedGovernments


Political &Economic

Base


DoDOrganization



MilitaryFacilities

NavyOrganization



MajorSubcontractors

ThreatGeography

OperationalForces

MissionRequirements

ProcurmentPrograms

Cost &Technology

RiskModeling

ThreatScenarios

OperationalConcepts

R&DPrograms

Major WeaponSystems


ThreatDatabases

WarfareRequirements



MajorSubsystems


DesignModel Base

TechnologyTestbeds


SystemTest

Requirements

TrainingObjectives


MissionSimulation

SystemSimulation

SystemTest

Facilities


TrainingFacilities


PerformanceAnalysis

Model Base

Publishers Update OverlayAftermarket OverlaysIntegration OverlaysLatest Information Overlays








INDUSTRY STANDARDSINDUSTRY STANDARDSINDUSTRY STANDARDSINDUSTRY STANDARDSIntegrating KnowledgeIntegrating KnowledgeIntegrating KnowledgeIntegrating Knowledge

Across Multiple SourcesAcross Multiple SourcesAcross Multiple SourcesAcross Multiple SourcesProvided By DifferentProvided By DifferentProvided By DifferentProvided By Different

DevelopersDevelopersDevelopersDevelopers

NEED TO MATCH CONCEPTS ACROSSNEED TO MATCH CONCEPTS ACROSSNEED TO MATCH CONCEPTS ACROSSNEED TO MATCH CONCEPTS ACROSSALL KNOWLEDGE SOURCESALL KNOWLEDGE SOURCESALL KNOWLEDGE SOURCESALL KNOWLEDGE SOURCES

Industry-wide Asset Integration.dsf Copyright Richard L. Ballard 1993-2006

Theory-basedTheory-basedTheory-basedTheory-basedSemanticsSemanticsSemanticsSemantics

25

Invented Logic as a Convention for Grammar Rules

"A noun is a sound that has meaning"A noun is a sound that has meaning"A noun is a sound that has meaning"A noun is a sound that has meaningonly by conventiononly by conventiononly by conventiononly by convention,,,,

there is there is there is there is no natural relationshipno natural relationshipno natural relationshipno natural relationship between any idea or observation and between any idea or observation and between any idea or observation and between any idea or observation and

the sound that you utter to describe it." the sound that you utter to describe it." the sound that you utter to describe it." the sound that you utter to describe it."

Aristotle Quote.dsf Copyright Richard L. Ballard 2006

Aristotle -- Aristotle -- Aristotle -- Aristotle -- On InterpretationOn InterpretationOn InterpretationOn Interpretation,,,, 350 B.C 350 B.C 350 B.C 350 B.C

No match-up of words to meaningNo match-up of words to meaningNo match-up of words to meaningNo match-up of words to meaningcan ever becan ever becan ever becan ever be --

Our commitment to assigning meaningOur commitment to assigning meaningOur commitment to assigning meaningOur commitment to assigning meaningto any idea must rest upon establishingto any idea must rest upon establishingto any idea must rest upon establishingto any idea must rest upon establishingits necessityits necessityits necessityits necessity and clear importance and clear importance and clear importance and clear importance

as a "as a "as a "as a "natural relationship." natural relationship." natural relationship." natural relationship."

provably provably provably provably right or wrong.right or wrong.right or wrong.right or wrong.

CONCLUSION:CONCLUSION:CONCLUSION:CONCLUSION:

26

Quinw Quote.dsf Copyright Richard L. Ballard 2006

"The ontology of a theory consists in the"The ontology of a theory consists in the"The ontology of a theory consists in the"The ontology of a theory consists in theobjects theory assumes there to be."objects theory assumes there to be."objects theory assumes there to be."objects theory assumes there to be."

Quine -- Quine -- Quine -- Quine -- Word and ObjectWord and ObjectWord and ObjectWord and Object, 1960, 1960, 1960, 1960

Theories are to be accepted or rejectedTheories are to be accepted or rejectedTheories are to be accepted or rejectedTheories are to be accepted or rejectedas a wholeas a wholeas a wholeas a whole....

If we choose to accept and use a theoryfor reasoning, then we must likewise

commit to all the ideas and relationshipscommit to all the ideas and relationshipscommit to all the ideas and relationshipscommit to all the ideas and relationshipsthe theory needs to establish its existence.

The The The The Orderly EvolutionOrderly EvolutionOrderly EvolutionOrderly Evolution of scientific knowledge requires that of scientific knowledge requires that of scientific knowledge requires that of scientific knowledge requires thattheoretical projections remain explicitly linked throughtheoretical projections remain explicitly linked throughtheoretical projections remain explicitly linked throughtheoretical projections remain explicitly linked through

those Ideas and Relationshipsthose Ideas and Relationshipsthose Ideas and Relationshipsthose Ideas and Relationships that their theories have assumed that their theories have assumed that their theories have assumed that their theories have assumed

If and when theories are rejected,If and when theories are rejected,If and when theories are rejected,If and when theories are rejected, their links dissolvetheir links dissolvetheir links dissolvetheir links dissolve....

27

A foolish consistency is the hobgoblin of little minds, .....

Speak what you think now in hard words, andto-morrow speak what to-morrow thinks in hard words again,

though it contradict every thing you said to-day.

Ralph Waldo Emerson -- Self Reliance, 1841

ON THE RACE TO A FIXED ON THE RACE TO A FIXED ON THE RACE TO A FIXED ON THE RACE TO A FIXED KNOWLEDGE STANDARDKNOWLEDGE STANDARDKNOWLEDGE STANDARDKNOWLEDGE STANDARD

Foolish Consistencies.dsf

28

29

Semantic Value Spectrum:Different capabilities power different levels of return

30

Semantic Bandwidth:Value gains from two-fold to more than 100 times

31

Semantic Wave Market Growth