Creating and Using Systems That Know - Anything - August 2008 Dr. Richard L. Ballard Chief...

Creating and Using Systems That Know

- Anything -August 2008

Dr. Richard L. BallardChief Scientist

Focus of Technical Briefing

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

By Dr. Richard Ballard

2. Development of That Theory Into A Third

Generation Knowledge Tool Advanced Engineering 1993 - 2004 Breakthroughs 2005 - 20083. Ballard / Shannon Limit Success

Ability to Store Unlimited Knowledge In Absolute Minimum Space

4. Constraint Browsing -- Axiology Portraying And Judging Every Human Value And Necessity

Briefing Focus Knowledge Foundations

Formulating A PreciseTheory of Knowledge

Knowledge = Theory + InformationDr. Richard L. Ballard 1987-1993

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

Knowledge As Evolutionary S cience

1 101 102 103 104 105 106 107 108 109 1010

Genetic Knowledge Storage (DNA Bits)

1

101

102

103

104

105

106

107

108

109

1010

1011

1012

1013

1014

Brain K

nowledge Storage (N

eural Bits)

Adapted from The Dragons of Eden , Carl Sagan, 1977

Humans

Mammals

Reptiles

Jellyfish

Virus

ProtozoaAlgae

Bacteria

Amphibians

Impl yingKnowl edge TypeFr omSt or age Type

Genes vs Brains

Ac

qu

ire

d T

he

or

y-b

ase

dK

no

wle

dg

e

Inst inct ive DNA Inher it ance

SENSE ORGAN r eceipt ofInf or mat ion pr oduces

physiol ogicalsit uat ion awar eness

-- w it h or w it hout a br ain.

BRAINLESS animal s r eac tusing t heir inst inc t ive dna

pr ogr ams -- t o succeed or die.

Badl y adapt ed spec ies die out .

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

Knowledge As Evolutionary S cience

1 101 10 2 103 10 4 105 10 6 107 10 8 109 1010

Genetic Knowledge Storage (DNA Bits)

1

101

102

103

104

105

106

107

108

109

1010

1011

1012

1013

1014

Brain K

nowledge Storage (N

eural Bits)

Adapted from The Dragons of Eden , Carl Sagan, 1977

Humans

Mammals

Reptiles

Jellyfish

Virus

ProtozoaAlgae

Bacteria

Amphibians

Impl yingKnowl edge TypeFr omSt or age Type

Genes vs Brains

Ac

qu

ire

d T

he

or

y-b

as

ed

Kn

ow

led

ge

Inst inct ive DNA Inher it ance

They const ant l y adapt"br ain cont ent (Theor y)"w it h no need t o change

t he host 's biol ogical f or m.

BRAIN memor ies model ,st or e, and t each

successf ul behavior sas "l essons l ear ned."

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

Amphibians

JellyfishProtozoa

AlgaeVirus

M odernC ivilization

Workgroups

S urvivalin S pace

Bacteria

Reptiles

Tur n Towar dA l l Theor ies --

A l l Know l edge

Logic+

In f or mat ion

SOFTWARE

HARDWARE

DOS Windows 98

486 PC

PentiumLaptop

386 PC

KNOWLEDGE AGEREQUIREMENTS

Knowl edgeC odes &Theor y

Model ingGap

Intelligent Animals Embrace M any "B ehaviorPatterns" For Their S elf-evident S uccess

1. Logical Sel f -consist ency insur es machine-l ike behavior , f ol l owing ext er nal mandat es ABSOLUTELY. 2. Their pr oof s possess no int r insic measur es of : effi c iency, r esour ce r equir ement , or compl exit y cost s .

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

3. Badl y mat ched t o a pr obl em, t heir cost s CREATE "non-comput abil it y."

Life vs. Logic

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 6 107 10 8 10 9 1010 1011 1012 1013 1014 10151016 1017

Human

Mammals

Amphibians

JellyfishProtozoa

AlgaeVirus

M odernC ivilization

Workgroups

S urvivalin S pace

Bacteria

Reptiles

Tur n Towar dA l l Theor ies --

A l l Know l edge

Logic+

In f or mat ion

SOFTWARE

HARDWARE

DOS Windows 98

486 PC

PentiumLaptop

386 PC

KNOWLEDGE AGEREQUIREMENTS

Knowl edgeC odes &Theor y

Model ingGap

Intelligent Animals Embrace M any "B ehaviorPatterns" For Their S elf-evident S uccess

1. Ext r emel y r esour ce awar e, t heir many possibl e goal s ar e al l Int ent ional , Compet it ive, Success-or ient ed, and of t en achievabl e in mul t ipl e ways.

2. They r ej ect opt ions t hat do not mat ch t heir sit uat ion or go against t heor ies t hey t r ust .

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

3. They expec t most choices ar e not pr ovabl y r ight or wr ong , seek t o enumer at e al l opt ions , and pr edic t t he consequences of each opt ion bef or e dec iding .

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"

-- Random House Dictionary

Everything Real &Observable

EverythingImaginable

E ver y "S c ienc e" E ver y "F antas y"

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

PracticalRationalism

ConceptsCategories

Epistemology

TheologyPure Intellect

Ontology

Axiomologies

Imagination

Form

Beauty

Truth

Logic

Mathematics

Natural Laws

Requirements

Design

Faith

Mark 3 Top-MostPrimitives

(HyptheticalModels)

(Acquired Models)

Ideas Theories Models Formalisms

Absol ut eBeing

P er f ec tInt el l igence

Ontological Primitives Cosmology

Chemistry

Biology

Sociobiology

Psychobiology

Energy

Matter

Machines

Planets

Animals

Galaxies

Local Groups

Societies

Particles

P hysicalUniver se

Sense DataPhenomena ObservablesEvents

Guanine

SKELETALDIGESTIVE

CIRCULATORYNERVOUS

C onceptualizing and OrganizingAll of Imagination and Reality

ENTITY

N-ARYRELATIONSHIP

THEORY-BASED MEDIATING STRUCTURE

n=2 n>2

1

2

3

"Knowl edgeTheor y-based

Semantic Web"

Copyright Richard L. Ballard 1994-2006

Metaphysics

TheologyCosmology

Chemistry

Biology

Sociobiology

Psychobiology

Energy

Matter

Forms

EmpiricismRationalismSense Data

Phenomena

Epistemology

Concepts

Materials

Machines

LANGUAGE

ART

Categories

Exemplars

Realistic

Beauty

Truth

Pure Intellect

NaturalLanguages

Mathematics

Logic

Measurement

IconsPhotographs

SY MBOLS& CODES

Ontology

The "a priori" rational constraintsof belief and accepted theory

Mental Concepts & Methodology Observed Reality

The "a posteriori" constraints ofobserved fact, material existence,

and recorded measurementPersistent

Representationsof Knowledge

Measured by InformationMeasured by Theory

Physics

PhysicalUniver se

Abstract

Univer sal s P ar t icul ar s

Absol ut eBeing

-Per f ec t

Int el l igence Planets

AnimalsX

Pro

ba

bil

ity

of P

redic

tin

gO

utc

om

es f

or

Every C

ho

ice

Pro

ba

bil

ity o

fR

ec

og

niz

ing

Sit

ua

tio

nC

orrectl

y

Pro

ba

bil

ity o

fK

no

win

g E

ver

yO

pti

on

Outc

om

eB

efo

re D

ecis

ion

sA

re M

ad

e

Kn

ow

ing

Cur

ren

t o

rH

ypo

theti

ca

lS

itua

tio

n

P(a

, b, c

,...

x, y

, z)

P(a

, b, c

,...

| ...

x, y

, z)

P(

... x

, y, z

)

SIT

UA

TIO

NP

RE

DIC

TIV

E W

EB

DE

CIS

ION

IM

PA

CT

FormalLanguages

Knowledge TheoreticRepresentations of Thought

Knowledge Theoretic Representation.dsf

Models

Situation ConstrainedNavigation of Every

Accepted Fact, Theory, &Predictable Decision Impact

Model Instance Codes.dsf Copyright Richard L. Ballard 2003

C onceptualism & S emanticsReplace Language

Properly implemented, S EM AN TIC W EB S approachthe absolute limits on s ize, speed, and efficiency.

Physics

Metaphysics

Model

Inst

a 1 - Nnces

Instance 0

PhysicalConcept

Inst

a 1 - Nnces

ModelInstance 0

Meta-PhysicalConcept

PhysicalUniver se

Absol ut eBeing

-Per f ect

Intel l igence

DataTypeModel

Inst

a 1 - Nnces

Instance 0

DataFormConcept

PersistentRepresentationsof Knowledge

Model - Instance Concept Codes Ar e Unique and Identical In Al l Languages

SEARCH is an artifact ofoverloaded symbol use.

In coded, declarative,semantic webs there

is no search of any kind.

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-based

Pr obabil ityof Pr edictingOutcomes forEver y Choice

Pr obabil ity ofRecognizing

SituationCor r ectl y

Pr obabil ity ofKnowing Ever yOption Outcome

Befor e DecisionsAr e Made

KnowingCur r ent orHypotheticalSituation

Physic al Theor y of Knowl edge & C omputat ion

P hysical Event of"Thought " or"Execut ion"

SemanticWeb

Real ity

SITUATION PREDICTIVE WEB DECISION IMPACT

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


P robabilis tic "P rac tic al R ationality"

Probabilistic Knowledge Theory A.dsf

X



G oalsEducationRespons ibilityRequirementsIntent

TimeRelation

ResourceOpportunity

Action

Theory-based

Pr obabil ityof Pr edictingOutcomes forEver y Choice

Pr obabil ity ofRecognizing

SituationCor r ectl y

Pr obabil ity ofKnowing Ever yOption Outcome

Befor e DecisionsAr e Made

KnowingCur r ent orHypotheticalSituation


P hysical Event of"Thought " or"Execut ion"

SemanticWeb

Real itya' pr ior i a' post er ior iDegr ees of F r eedom & Const r aint


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

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


P robabilis tic "P rac tic al R ationality"

Probabilistic Knowledge Theory A.dsf

Theory-basedSemantic Web Real ity


P(a, b, c ,... x, y, z) P(a, b, c ,... | ... x, y, z) P( ... x, y, z) Fundament al Ul t imat e L imit Measur es

K NOWLEDGE = THEORY + INFORMATION

Copyright Knowledge Foundations 2006Quantitative Hard Science.dsf

Knowledge As AQuantitative Hard S cience




a' pr ior i a, b, c, ... ... x, y, za' post er ior iDecision Success P(task)

Theory-basedSemantic Web Real ity


P(a, b, c ,... x, y, z) P(a, b, c ,... | ... x, y, z) P( ... x, y, z) Fundament al Ul t imat e L imit Measur es

K NOWLEDGE = THEORY + INFORMATION

Copyright Knowledge Foundations 2006Quantitative Hard Science.dsf

Knowledge As AQuantitative Hard S cience

Knowl edge Theor y-basedUl t imat e Minimum

Dec ision Resour ce Cost

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

ShannonInf or mat ion Bandw idt h

& St or age L imit Cost

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

Bal l ar d Educat ion, Web Cer t ifi cat ion,

& Theor y Capt ur e L imit Cost

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

Bal l ar d Educat ion, Web Cer t ifi cat ion,

& Theor y Capt ur e L imit Cost

-log{P(a, b, c,...|....x, y, z)}Theor y pr edic ts thatc os ts can & will s c ale

pr opor t ionally toInfor m at ion C ontent

Theor y pr ovides per fo r m anc e-bas ed m eas ur es c om par ing

E duc at ion, Theor y C aptur e, &K nowledge C r eat ion inves tm ent

Theor y links tas k s pec ifics uc c es s es to m os t effec t ivet r ade-offs in t r aining, theor yc r eat ion, & tec hno logy us e




a' pr ior i a, b, c, ... ... x, y, za' post er ior iDecision Success P(task)

On Creating A Third Generation

Knowledge Tool Advanced Engineering 1993 - 2004 Breakthroughs 2005 - 2008

Structured Storage

SERVERPROCESSORS

PrimitiveCache

Mk 3Full

Server

FoundationsEXE

ConceptCache

Mk 3ConceptHandler

&In-ProcServer

FoundationsDLL

F il e 0

F il e 2

F il e 3

F il e 4

F il e 6

F il e 8

L ay erD isk

D rive sFile

Buffers

"Words"

Mk

3 C

lie

nt

Bro

ws

er/

Fin

de

rM

k 3

Cli

en

t B

uil

de

r

Ar chitectur e ofConcept Knowl edge Fl ow


Mark 3 Knowledge Flows 6 8x11.dsf

OPEN MFC COMPONENT

Base Class

CBuildMark3Layer

KnowledgeLayer

Formatting& EditorialRescaling

1

DLL

2

FS.EXE

3

4

5

MARK 3 ALPHA BACKEND

C:/

FOUNDATIONS KB LAYER 1

KB LAYER 2

SOURCES

EDIT STACK

ONTOLOGY OVERLAY

DEV HISTORY OVERLAY

DOC1

DOC2

DOC3

EDITOR1

EDITOR3

EDITOR2

DEVTOOLS

USERTOOLS

COMMON FILES

KB LAYER 3

KB STACK A

KB STACK B

FOUNDATIONS Directory Tree

WORKSPACES

USER1

USER2

USER2

1024 x 670Client Area

Sta rt Type to se arc h 11 :58 PMM edic al Guide Found a tions BRO WSER

File View Window Help

C oncep t: N et w or k/ Tr ee View s: Time Cl uster ConceptNet wor k

Foundat ions Br owser -- [Medical Guide]

SELEC TED CONC EPT RE LA TIONSHIP S PA TH NA MES

ControlDashboard

IMA GES

DE FINIT IONS

CenteredPrimaryOptionLeft

SecondaryOption

Left - mostSecondary

Option

RightSecondary

Option

1024 x 670Client Area

Sta rt Type to se arc h 11 :58 PMMe dica l Guide Found a tions BROWSER

File View Win dow He lp

C oncept : N et wo r k/ Tr ee View s: Time Cl ust er ConceptNet wor k

Foundat ions Br owser -- [Medical G uide]

SELEC TED CONCEP T RELA TIONSHIPS PAT H NAM ES

ControlDashboard

Continuous Variable Cost

Explicit Variable Cost

Variables in Order of Importance

No Correl ati onsKnow n

Wea k Corre lat ion'sNegle cte d

Rema ini ng StrongCorrel ati ons

Paramet ersChos en

Describe

Concept:

10K 1K 100 10 1

Possible C oncept Mat chesTot alC oncept s

12,157

14121086420

Descr iptive Inf ormation Content (bit s)

Advice:

WordRecognition:

PossibleWor ds

Wor d describes concept

Wor d is the concept

Concept

Selection:

Pot ent ialList S ize

Foundations Builder - [Concept Finder -- Basic]

C losestMatches

10

CopyLi st

TOPC oncept s

10

CopyLi st

Use This Synonym Ant ony m Rest ore

Rest oreTa g Fi lter

SELECT CANCEL

CON REL DFMStorage Storage Storage

CO

NC

EP

T

RE

LA

TIO

NS

HIP

DA

TA

FO

RM

ROOT STORAGE

MARK3 STRUCTURED LAYER

LNG

LA

NG

UA

GE

ED

IT S

trea

ms

BROWSER

BUILDER

Knowl edge

Edit For msTrans lating K now ledge into

Patterns of Thought

Dr. Richard L. Bal lard

ENTITY

N-ARYRELATIONSHIP

THEORY- BASED MEDIATING STRUCTURE

n=2 n>2

1

2

3

"Kn ow l ed geTh eo r y-bas ed

S ema nt ic W eb "

Copyr ight Richa rd L. Ballar d 19 94-2 006

Metaphysics

TheologyCosmology

Chemistry

Biology

Sociobiology

Psychobiology

Energy

Mat ter

Forms

Em pir icismRationalismSense Da ta

Phenomena

Epist emology

Concepts

Mat eri als

Mac hines

LANGUAGE

A RT

Cate gories

Exemplars

Realis tic

Beauty

Trut h

Pure Int ellect

NaturalLa nguage s

Mat hematic s

Logic

Mea sureme nt

IconsPhot ographs

SY MBOLS& CODES

Ont ology

The "a priori" rational constraintsof belief and accepted theory

Men tal Concepts & Methodo logy Observed Real ity

The "a posteriori" constraints ofobserved fact, material existence,

and recorded measurementPersistent

Representationsof Knowledge

Measured by I nformationMeasured by Theory

Physic s

Ph ys icalUniver s e

Abst rac t

Un iver s al s P ar t icu l a r s

Ab so l ut eBe in g

-

P er f e ctIn t el l ig e n ce Planets

Animals

FormalLa nguage s

XP

rob

abil

ity

of

Pred

ict

ing

Ou

tco

mes

fo

rE

very

Ch

oic

e

Pro

bab

ilit

y of

Rec

ogn

izin

gS

itua

tion

Co

rrec

tly

Pro

bab

ilit

y of

Kn

owin

g E

very

Opt

ion

Ou

tco

meB

efor

e D

ecis

ion

sA

re M

ade

Kn

owin

g

Cu

rren

t or

Hyp

oth

eti

cal

Sit

uati

on

P(a,

b, c

,... x

, y, z)

P(a,

b, c

,... |

... x

, y, z

)P(

... x

, y, z

)

=

SIT

UA

TIO

NP

RED

ICTI

VE

WE

BD

ECIS

ION

IMP

ACT

Mo dels

FUNDAMENTAL DEFINITIONSin

Knowledge Science & Engineering

Dr. Richard L. BallardDecember 2004

FIRS T COURS E IN K NOWL EDGE ENGIN EE RING

Creating Systems That Know

CREATING A NEW SCIENCE 1970-2005

KNOWLEDGE FOUNDATIONSMARK 3 VERSION 1 ALPHA

BROWSER Fact Sheets

COPYRIGHTDr. Richard L. Ballard

February 2007 - September 2007

CODING MOST SECRET FU TU RE ENTERPRISE & INVENTION

ArchitecturesArchitecturesMark 3 vs ConventionalMark 3 vs Conventional

Databases

Operating System

• KFI - Mark 3Theory-based Knowledge Integration

• Conventional 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 Overlay

Aftermarket Overlays

Integration Overlays

Latest Information Overlays

Requirement & Assumption Overlays

User Work Products & Overlays Layer

User Work-In-Progress Layers

Validated Workgroup Baseline

USER'S WORKING LEVELS

Published Reference Stack

Proprietary Knowledge Assets

WORKGROUP SHARED ASSETS

CORPORATE KNOWLEDGE ASSETS

Integrate K nowledgeProduced By

ALL PUBLISHERS

NEED TO MATCH CONCEPTS ACROSSALL KNOWLEDGE SOURCES

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

Theory-basedS emantics Mar k 3, Ver sion 2: Design Obj ec t ive

INDUSTRY STANDARD

To Begin The Process Of AssessingThe Impact Of Every S ituation

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

S it ua t io n

4

Lo cat ing

5

6

Tar get

7

W ha t is Th er e?

8

Tak es ou tJ -5 t oo

W ho h as g ot o ne?

20 M at ch in gNA T O J ammer

B an d19

J us t th e B ad Gu ys

18

W hoel se?

17

W ha tf r equ enc ies ?

1615

R ad ar ? 15A r mo r ed,

Sh oo ts bigger ,f ar t her &

f as ter t han us

To p Gu n

Can he see us?

14 13Hea r t o fDa r kn es s

12

W h er e is hisw eak nes s ?

Or ga niz at ion& E q uipmen t

11Nig ht mar e, W h enF l ying l o w !

10W h o is s ho ot ing

at Us?

9

W ho c ar r iest his s yst em?

22 W ha t el sedo you ca r r y?23 I wa nt t his guy

w ith u s!

M ou nt t heban d 10

j ammer po d!

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

A ir 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 is

the 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 OptionAnd 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

S it ua t io n

4

Lo cat ing

5

6

Tar get

7

W ha t is Th er e?

8

Tak es ou tJ -5 t oo

W ho h as g ot o ne?

20 M at ch in gNA T O J ammer

B an d19

J us t th e B ad Gu ys

18

W hoel se?

17

W ha tf r equ enc ies?

1615

Rad ar ? 15A r mo r ed,

Sh oo ts bigger ,f ar t her &

f as ter t han us

To p Gu n

Can he see us ?

14 13Hea r t o f

Da r kn es s

12

W h er e is hisw eak nes s ?

Or ga niz at ion& E q uipmen t

11Nig ht mar e, W h enF l ying l o w !

10W h o is s ho ot ing

at Us?

9

W ho c ar r iest his s yst em?

22 W ha t el sedo you ca r r y?23 I wa nt t his guy

w ith u s!

M ou nt t heban d 10

j ammer po d!

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


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

A ir 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 is

the 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)

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

... x, y, z SITUATION

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

CONOPSOption #1

N = 4DECISIONS:

THEORYN = 7

Decisivedegrees

offreedom

Copyright Knowledge Foundations 2006Semantic Web 2.dsf

On AchievingBallard / Shannon

Limit Success Ability 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, and unlimited knowledge capacity. No other tool can do this.

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

Mark 3 supports a non-object oriented, theory-based description of knowledge, 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 theories and objects.

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

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

Knowledge Layers from Many Sources

EmploysConstraint Browsing

- Axiology - Portraying And Judging Every Human Value And Necessity

Constraint Browsing

Medical 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.


Concept : Const r aint Br owser Views: Time Cl uster ConceptConstr aint

F oundat ions Br ow ser -- [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 AlergyThrombo-cytopenia Measles

ScarletFever

ChickenPox

LymeDisease

Parvo-virus Rubella Pneumonia

AcuteBronchitis

Emergency UrgentHelp

Bring DownFever

CallDoctor in24 hours

MedicalHelp

Self-HelpBring Down

Fever

Self-HelpHome

Pregnancy

SoreThroat

ContinuePrescription

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 Well

Start 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.


Conc ept : Const r aint Br owser Views: Time Cl ust er Conc eptConst r aint






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.


Concept : Const r aint Br owser Views: Time C l ust er Conc eptConst r aint






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.


Concept : Const r aint Br owser Views: Time Cl uster ConceptConstr aint






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.


Concept : Const r aint Br owser Views: Time Cl uster ConceptConst r aint






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

Urgent

Urgency

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.


Concept : Const r aint Br owser Views: Time Cl uster ConceptConst r aint






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 AlergyThrombo-cytopenia Measles

ScarletFever

ChickenPox

LymeDisease

Parvo-virus Rubella Pneumonia

AcuteBronchitis

Emergency UrgentHelp

Bring DownFever

CallDoctor in24 hours

MedicalHelp

Self-HelpBring Down

Fever

Self-HelpHome

Pregnancy

SoreThroat

ContinuePrescription

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

Date post:	16-Dec-2015
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