Knowledge Foundations

Transcription

FUTURE REVOLUTIONS
IN CONTENT
Dr. Richard L. Ballard
Chief Scientist
Knowledge Foundations, Inc.
Seybold 365
San Francisco, CA
September 8-12, 2003
2
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
Copyright Richard L. Ballard 2003
Seybold 365 Conference Bullets.dsf
3
Knowledge Trumps Programming
Learning Lessons from
Animal Evolution
Seybold 365 Knowledge Trumps.dsf
4
14
10
13
10
12
11
Humans
Mammals
10
10
10
10
9
10
8
10
7
10
6
10
5
10
4
10
3
10
2
10
1
1
Reptiles
Genes vs Brains
Implying
Knowledge Type
From
Storage Type
Amphibians
Theoretical Knowledge
Brain Knowledge Storage (Neural Bits)
10
Understanding
Knowledge
Adapted from The Dragon's of Eden, Carl Sagan, 1977
BRAINS Produce
Memory,
Adaptive
Learning,
Knowledge-Based
Experience,
Behaviors change
to model success
THEORY &
LEARN - new theory
Teaching
from experience
TEACH - theory in
extended childhood
INFORMATION
Produces
Jellyfish
Instinctive
Protozoa
Situation
Information-Based
Algae
Awareness
Bacteria
INSTINCT - DNA
Virus
programed responses
&
to sensory awareness
Instinctual Knowledge
Instinctive
NEVER LEARN Response
simply compete,
12
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)
best suited survives
Seybold Biological Knowledge Types.dsf
Copyright Richard L. Ballard 1998-2003
5
Knowledge = Information + Theory
KNOWLEDGE AGE
REQUIREMENTS
10 21
Survival
in Space
10 20
10 19
10 18
10 17
Modern
Civilization
Workgroups
10 16
10 15
Turning From
Information
Toward the
Coding of
Theory
10 14
Human
10 13
Mammals
Reptiles
10 12
10 11
Pentium
Laptop
10 10
10 9
HARDWARE
10 8
486 PC
10 7
386 PC
Single Coding
System
for
ALL
KNOWLEDGE
10 6
10 5
10 4
Amphibians
Jellyfish
Protozoa
Bacteria Algae
Virus
Logic
DOS
1
10 3
Turn Toward
All Theories -All Knowledge
SOFTWARE
+
Information
Windows 98
10 2
10 1
1
10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 1015 1016 1017 1018
Seybold Civilzation & Computing.dsf
Information Age
Software Has
Fixed Behaviors &
Must Be Replaced.
It Has No Capacity
to Learn & Change.
6
Economics of Knowledge Form
A' Priori
A' Posteriori
Theory
Information
+
What You Can Learn
Once & Know Forever
Answers to:
WHY?
HOW?
WHAT IF?
Up-to-the-Moment
Situation Awareness
est.
85%
est.
15%
Answers to:
WHO?
WHERE?
WHEN?
WHAT?
HOW MUCH?
Costs:
Costs:
Invest Once
Employ Forever
Loss:
Dies, if left only
in Human Heads
Pay A Little
Every Day
Loss:
Old News
Tomorrow
Seybold Economics of Knowledge Form.dsf
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Creating A New Science and Technology
To Bridge from Information Age to Knowledge Age
KNOWLEDGE TECHNOLOGY
Cost of
Knowledge
crash
1984
Productization,
Industry
Formation
& Emergence
Preliminary
to Advanced
Development
Physical Theory of
Knowledge &
Computing
1993
Knowledge
Technology
Today
20 years Back
0
10
Deployment
Basic &
Exploratory
Research
20 years Foreward
20
30
Fiction Game Engines
Space & Defense
& "Star Wars"
Programs Management
Battle Management of National Importance
Key Research and Development Sponsors
Seybold Technology Transition.dsf
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KNOWLEDGE TECHNOLOGY
-- Essential Characteristics
Unchanging Programmed Base becomes Fixed Hardware Base
Current Record: 13+ years, no recompile Windows 2 to Windows XP
Disappearance of CPU and logic centric processing
Expectation:
Very Slow Evolving, Subject Independent, All Knowledge Codes
Current Record: 20+ years, Mx Semantic Concept Codes
Expectation:
Early Evolution focused in the Linguistic - Semantic Gap
Professions, Publishers, & Government Lead Ontology Codification
Current Record: Minimum asset ontology 5K-50K concepts, schema are dying
Expectation:
Ontologies will top 50-500 million concepts in next 10 years
Theory 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
Seybold Essential Knowledge Technology.dsf
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SUMMARY
Machine Theories and Computation will
Model Human Theories and Reasoning
Knowledge Content is a Capital Asset
Knowledge Ownership and Practice
Will Define an Organization's Value
Knowledge Permanence & 30-100 year Change Cycles
Frame Revolutionary New Vistas for World Planning
Seybold Knowledge Trumps Summary.dsf
10
Semantics Trumps Linguistics
Representation as
a Precise Science
Seybold Semantics Trumps.dsf
11
Philosophy -- Language for "All Knowledge"
"Semantic Networks"
1
RELATIONSHIP 2
ENTITY
n=2
n>2
et
hy
p
a
c
si
s
Ph
ys
LANGUAGE
MEDIATING
3
STRUCTURE
(taxonomy,
Natural
Epistemology Formal
aggregations,
Languages Languages
network.
Ontology
flow. QFD,..)
Pure Intellect
M
ic
s
Chemistry
Biology
Psychobiology
Sociobiology
Abstract Realistic
Absolute Theology
Cosmology
Being
Physical
Empiricism
Rationalism
ART
Universe
Categories
Sense Data
Perfect Forms
Models Exemplars Phenomena Planets
Intelligence
Concepts
Beauty
Universals
Icons
Truth
Logic
Measured by Theory
Mental Concepts & Methodology
The "a priori" rational constraints
of belief and accepted theory
Photographs
SYMBOLS
& CODES
Mathematics Measurement
Persistent
Representations
of Knowledge
Animals
Machines
Matter Materials
Energy
Particulars
Measured by Information
Observed Reality
The "a posteriori" constraints of
observed fact, material existence,
and recorded measurement
Semantic Modeling as the Means of Knowledge Capture
12
Conceptualism & Semantics
Replace Language
Model - Instance Concept Codes
Same for All Languages
s
ic
s
hy
p
a
et
M
Ph
ys
ic
s
MEANING is defined only by the unique
web of relationships tying every concept
to those connecting to it.
MetaPhysical
Instances
Model
Absolute
Being
Models are metaphysical
and typically have
Perfect
plural concept titles
Intelligence
Instance 0
Model
1-N
Physical
Universe
Physical
Instances
SEARCH is an artifact of
overloaded symbol use.
In coded, declarative,
semantic webs there
is no search of any kind.
1-N
Instance 0
The Reality Web &
Semantic Web are
fundamentally different,
but will interact constantly.
Persistent
Representations
of Knowledge
A CONCEPT (model-instance) appears
only once in any semantic web, the
codes locate it instantly without search
Properly implemented, SEMANTIC WEBS approach
the absolute limits on size, speed, and efficiency.
Seybold Model Instance Codes.dsf
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Adapting Semantics to
All Theories Representation
d
ar
d
n
ta
S
l
ca
i
ALL
h
op
s
KNOWLEDGE
ilo
h
P
n
tio
a
t
Independent
n
se
e
r
p
Re
c
Relative
ti
an
m
Se
Ballard/Sowa/Peirce Synthesis
Time
Indep.
Time
Dep.
Physical
L e ve l 1
L e ve l 2
Mediating
Law
Observe
Faith
Theory
L e ve l 3
Relationship
Mediating Structure Taxonomy
Taxonomy
Concept
Taxonomy
Metaphysical
Relational
Subjects
Constraints
Involved
Contributor: John F. Sowa
Patterns of Organization
14
12 or 18
Primitive Concept
Types
SUMMARY
Theories of Knowledge and Computation
Define an Emerging Science of Representation
Centered on Semantic Form that Science
Solves Seemingly Unsolvable Complexity Problems
Pure Declarative, Semantic Webs Have Been
Tested for 12+ years in Significant Applications
First Projects Should Begin Emerging Commercially
in 2004-6 as Desktop Product Discs and Downloads
Seybold Semantics Trumps Summary.dsf
15
Publishing Models Lead Knowledge Capture
Amassing & Organizing
A Dominating
Reference Source
Markets, Sources, & Production
Seybold Publishing Leads.dsf
16
Semantic Knowledge Layers
Ellington
Organization
Aircraft
Systems
NASA
T-38A
JSC
T-38N
Ellington Fld
KC-135R
Manufacturers
WB-57F
Pilots/Chiefs
Tail Number
DoD
Organizations/Personnel
FltStatus
Avionics
Tacan
JEDS
Discrepancies
Aircraft
YE AR
Office of the
Director - JSC
Director Flight Crew
Operations
Director Adminis tration
O ffi ce o f th e Di re c to r
F lig h t C re w O p e r a tio n s
C h ie f S p a ce S ta tio n
S u p p o rt O ffic e
Part Number
PART REMOVED
Part Number
PART INSTALLED
Tail Number
Fli gh t Statu s
NA SA OR GAN IZATIO N
D e r i v a ti v e s
T -38A
Work BreakDown
T ai l N um b e r
D iscre p an cy
R e po rt ID
F -5
M o d el S e q u en c e
A T -38
Part Number
FLIGHT STATUS
Da te
T -38
V a r i a n ts
Tail Number
Co rrective Actio n
AIRCRAF T STATUS
Tail # 900-944
C h ie f A s tro n a u t
O ffi ce
Maintenance &
Repair History
Date
Director Mission Operations
C h ie f S p a ce S h u ttle
S u p p o rt O ffic e
Theory Reuse
> 90%
HELP
Corrective Action
Fly Fly Some
Grounded
No
Problem Dis crepanc ies
Da ys
D ir e cto r Jo h n K e n n e d y
S p a c e C e n te r
BACK
Flight S tatus
Work Orders
M on th s
CALENDAR
D ir e cto r L yn d o n J o hn s o n
S p a ce C e n te r
Reports
Inspections
Corrections
Check-out
Tail Number
Administrator
NASA Headquarters
D ir e cto r D ir e cto r G e o r g e M a rsh a l l
A m e s R e se a r ch S p a ce F li g h t C e n te r
C e n te r
Tasks
Indications
Diagnosis
Recommend
Calendar
Flights
Check Flights
Aircraft Status
Set of
Part Changes
Wiring/Fault Diagram
REPAIR PROCEDURE
D A IL Y R E P O R T
T -38N
L o t S e q u en c e
L ot 1
Crew
Chiefs
Pilots
L ot 2
P il ot N a m e
Executive
Offices
Electronic Aircraft Division B-2 Division
Systems Division - Northrop
- Northrop
Northrop World Wide
Aircraft Services
T-38
Airframe
NASA
Organization
Corporate
Staff
T-38 / F-5 Program
Office
F IL E D B Y
T- 3 8N
T- 3 8N
T -3 8 N
T- 3 8 N
M o ni tor in g &
Co n tro ls
N a vi ga ti o n
Id e nti fica tio n
R ec or d in g
& Di sp la ys
T-38N
Single
Function
Control
T-38N
T-38N
MultiFunction Multifunction
Control
Display
T-38N
Weather
Radar Display
S e cre ta ry of D efe n se
Office of the
Secretary
of Defense
Secretary of
the Air Force
Chief Scientist
of the Air Force
Secretary of Secretary of
the Army
the Navy
Commander
San Antonio
ALC
USAF Materiel
Command
Aircraft
Management
Directorate
Air Training
Command
Air Combat
Command
3246
Tactical
Wing
6510
Tactical
Wing
T -3 8 N
L o w e r
L B a n d
A n te n n a
T-38
Systems
Manager
T-38N
Radio
ARN-147(V)
Nav Receiver
VOR/LOC/GS/MB
T- 3 8N
T- 3 8N Ta r ge t /
Re co nA cq u isiti on
& S trik e
T-38N
Self
Contained
ARN-154
NAV Receiver
(TACAN)
T-38
Avionics
Manager
Display
REPORTED FIX
Discrepancy
Report ID
Crew Chief
Recommended
Action
Fault Indication
Set of
C o rre ctiv e A c tio n s
Corrective Action
REPORTED FAULT
Fa u lt D iag n osis
Set of
Fault Diagnosis
EQ UIP DIA GNOTICS
T -3 8 N
R F
L in e
S w it c h
T ACAN INST AL LAT IO N PRO CEDURE
Work Breakdown
Structures
Set of
R e co m m e nd e d
R e m e d ie s
Audio
Control
Panel Front
Audio
Control
Panel Rear
Symbol
Generator
No 1
Control Flow
RF Signal Flow
T -3 8 N
U pp e r
L B a n d
A n te n n a
T ACAN REM OVAL PRO CEDURE
T ACAN P REFL IG HT TES T P ROCEDURE
RECO MM ENDED FIX
Display
F au lt In d ica tio n
...
On Tacan,
Gain access
to right hand
disconnect
avionics bay
I/O multi-pin
Cut safety
On Tacan, cable
disconnect
wire from
retaining bolt
coax antenna cable
Symbol
Generator
No 2
Upper
L Band
Antenna
T -3 8 N
TAC AN
T r a n s c e i v e r S y s te m
A R N -1 5 4
N A V R e c e iv e r
(T A C A N )
T A C A N S Y S T E M /S U B S Y S T E M
Tactical Trainer Systems
Program Management
T-38
Modifications
Manager
T-38N
Radio
Management
T-38N
Radar
Discrepancy
Report ID
Corrective Action
Aircraft
System/Subsystem/Parts
Chief of Staff
of the Air Force
San Antonio
Air Logistical Center
Aircraft
Program
Managers
T-38N
Loadouts
T-38N FUNCTIONAL DECOMPOSITION
R O CK W E LL IN T ER N A TIO N A L
M AGN AVOX
Parts
Manufacturers
T-38N
Avionics
T- 38 N
Da ta
Ha n dl in g
NORTHRUP GRUMMAN
M OTOROLA
T-38N
Propulsion
Discrepancy
Report ID
Set of
Discrepancy
Reports
D is c re p an c y
R e po r t ID
T-38N
Northrop Grumman Corporation
Board of
Directors
ARN-154 Transceiver
Removal Procedure
Crew Chief
Da te
.. .
T -3 8 A IR C R A F T D E S IG N
SPACE SHUTTLE SUPPORT OFFICE
Lower
L Band
Antenna
T-38N
RF TRANS
Line Switch
Wiring Diagram
T-38N
28 Volt
Fault Tree DC Power
DE PA RT ME NT O F D EF ENS E
Subscription
Information
Layer
Downloads
A R N -1 5 4
N A V R e c e iv e r
(TA C A N )
Serial Data Flow
KDA 1
Data
Adapter
KDA 2
Data
Adapter
Power Flow
T-38N
Avionics Bay
DC Circuit Breaker
Panel
Navigation
Management
Unit
TACAN WIRING DIAGRAM
By PROFESSION or INDUSTRY
Complete Working
Knowledge
Seybold Working Knowledge.dsf
17
T-38N
OPERATIONS &
MAINTENANCE
Ellington Field,
Houston, TX
Asset Value Experience
Competitive Knowledge Superiority
Overwhelming speed, knowledge, and predictive success
at forward points of contact, sales, support, and supervision
Productive Labor Category Transitions
Knowledge asset use to gain productivity by transitioning
from professional to para-professional labor categories
Adaptive Learning & Experience Gain
System awareness of failures and capacities to reason,
learn, and invent more successful theories and behavior
Knowledge Creation & Assured Ownership
Organizational self-knowledge of assets and predictive success
at creating new assets and opportunities
Seybold Value Experience.dsf
18
Knowledge Asset Modeling Teams
Project Manager / Product Designer
Responsible to customer requirements, schedule, budget,
development plan, product design, and project goals.
Rational Modelers
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.
Outliners / Transformers / Production
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.
Knowledge Editors
Enforces product design, artistic, granularity,
abstraction, and best practice constraints.
Directs content integration, language selfconsistency, and subject specialist review.
Tests accuracy, completeness, and form with
automated tools.
Full Time Commitments
Seybold Modeling Teams.dsf
Subject Specialists
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.
19
PRODUCTION PROCESS
Full & Part-Time
"Editors"
6
Text Books
1
3
Documents
Data Bases
"Outliner"
8
4
2
7
Sponsor's
Consulting
Content
"Experts"
9
5
"Modeler"
Word Processor
SOURCE AQUISITION & MODELING
10
INTEGRATION
& ONTOLOGY
Set of R equired Systems
Avionics
Functional Areas
Radio
Navigation
Categories
Radio Navigation
Communications
Navigation
Identification
Monitoring & Recording
Data Handling
Reconnaissance
LF
HF
VHF
UHF
Auto Direction Finder
Direction Measuring
Flight Track Navigation
Global Positioning
Microwave Landing
Navigation Receiver
Radio Altimeter
Radio Beacon
Radio Compass
Radio Receiver
Radio Transmitter
Station Keeping
TACAN
Traffic Alert/Collision
Avoidance
GPS
GS
ILS
LOC
LORAN
MB
OMEGA
SHORAN
TACAN
VOR
Radar Navigation
Target Acquistion & Strike
Self-Contained Navigation
Electronic Combat
Abstraction
Hierarchy
System Attribute Values
APN-157
AR N-12
AR N-14
AR N-18
AR N-32
AR N-58
AR N-67
AR N-78
AR N-82
AR N-84
AR N-92
AR N-108
AR N-109
AR N-120
AR N-123
AR N-127
AR N-131
AR N-136
AR N-139
AR N-147
CMA-764
ED O-1200
GNS-500
ILS-70
KLN -670
KN S-660
KTU-709
LORAN C
LTN-211
LTN-2001
MKA-28
TCN-500
TDL-800
UNS-1
VIR-30
VIR-31
VIR-34
VN -411
VN S-41
VOR-101
RATIONALE TEST &
PRODUCTIZATION
All Models
All Sources
20
System Decomposition
System Data Flow Diagrams
15
16
18
SystemTest Procedures
System
Model
Flight Control
Controls & Displays
11
51R System Function Classification
51V
51Z
APN-70
APN-151
Avionics
Function
EDITING &
MODEL
VALIDATION
21
LANGUAGE
Epistemology Formal
Languages
Ontology
Natural
Languages
Chemistry
Biology
19
14
17
Psychobiology
Pure Intellect
Sociobiology
Abstract Realistic
Absolute Theology
Cosmology
Being
Physical
Empiricism
Rationalism
ART
Universe
Categories
Sense Data
Forms
Perfect
Models Exemplars Phenomena Planets
Intelligence
Concepts
Beauty
Universals
Icons
Truth
Measured by Structure
12
Models
Seybold Production Process.dsf
Logic
Animals
Machines
Photographs
SYMBOLS
& CODES
Matter Materials
Energy
Particulars
22
Mathematics Measurement
13
Persistent
Representations
of Knowledge
23
24
User
Guide
BUILDER
20
Source Acquisition & Rational Forensics
Modeling Significant Document Structures
Budget
Categories
6.2
6.3
DTAP &
DTO Funding
1997
Technology
Areas
Time Periods
10 Areas
FY 1997
1995
2000
2005
2010
Technology
Area Plans
Assumed
Baselines
Area Total
Tech Sub-Area
10 plans
Develop. Effort
Technology
Sub-Area
Plans
Time Period
Tech Sub-Area
Baseline
Platform
Product
Capability
Deficiency
Scenario
Fiscal Year
DTO Support
Sub-Area Roadmap
2 -14 / Area (5 median)
Transition Goals
Baseline
Attribute Set
Improvement
Sought (%)
Pay-off Objective
Seybold Mapping Document Structures.dsf
National
Objectives
Security
Objectives
Sub-Area
Development
Efforts
Programmed
PE Effort
Adv Tech
Demos
(ATD/ACTD)
Military
Objectives
Campaign
Objectives
Operational
Objectives
Defense
Technology
Objectives
Performance
Attribute Sets
Technology Base
Metrics of
Performance (MOP)
STRATEGY
TO TASK
Program
Elements
KRC Doc # 36
Tech Area
Budget
Performance
Theory
N=5
Defense Technology
Area Plan (DTAP)
April 1996
Policy
Trade-Off
Theory
N=14
Operational
Tasks
Government
Programs
Industry
Programs
Challenge
Statements
21
Key
Attribute
Requirements
qfd
Publishers Take the Lead with Semantics
Impossible
Execution &
Database Costs
Semantics Controls
Complexity Growth
Going from
Information to Theory
Explosive
Complexity Growth
~N2
Entity
N=1
ar In ~N
e
n
i
L
t
Are
nten
s
t
o
s
C
n
Co
atio
m
r
Info
5
10
15
N
Assumed
Baselines
Tertiary
Relation
N=3
Area Total
Time Period
Baseline
Platform
Product
Capability
Deficiency
Scenario
Transition Goals
Baseline
Attribute Set
Improvement
Sought (%)
Lot1 Lot 2 Lot 3 Lot 4 Lot 5 Lot 6
Lot 7
Max Aircraft / Month
Production Rate
A ircraft
Total Cost
N-ary
Relation A ttributes
N=2,3,4,
5, 6, 7,......
DTO Support
Tech Sub-Area
Aircraft Lot
Aircraft Model / Block
Binary
Relation
N=2
CONTENT -- JUST ANOTHER THEORY .....
Performance
Theory
N=5
C-130A
Airframe Cost
Propulsion Cost
Avionics Cost
THEORY
as N-ary Bundle
N -- Informational
Conditions
Outcome Predicted
by Theory
Angle of Climb
Altitude
Roll Rate
Air Speed
Test Aircraft
ROLL MEASUREMENT
Pay-off Objective
Seybold Semantic Master Complexity.dsf
22
SUMMARY
Theories Remain Relevant for 1000s of Years,
Independent of Facts & Language Changes
that Make Them Appear Different
The Marshalling and Authentication of Any Body of
Theory is a Capital Expense Conveying Ownership &
Creating Substantial Barriers to Competition
Dominating Theory Positions in Economically
Significant Markets Create the Frameworks
for Structuring All Tasks & Information Use
Publishers Already Have the Business and Staff
Models to Transform Existing Assets into Products
Seybold Publishers Lead Summary.dsf
23
Enterprises Provide Employee Knowledge
Who Will Own The
Company's Knowledge?
Seybold Enterprises Provide.dsf
24
Mastering Situation with Intent
Management:
Making things happen
that would never happen
by themselves
Seybold Mastering Situation.dsf
25
New Options and Resources
For Getting Jobs Done
EXPRESSED
ORGANIZATIONAL
INTENT
REACTION &
FEEDBACK
Theory Constraints
Education & Experience
Job Requirements
Concepts of Operation
Plans & Task Theories
Management Tools
Resource Control
Reality Constraints
Employee
Manager
Different Transmitted
Orders & Intent
Seybold Enterprise Options.dsf
Situational Information
Task Instructions
Workload & Progress
Discretionary Resources
Assigned Resources
Non-work Commitments
Different Reactions
& Feedback
26
Enterprise Knowledge Stacks
Virtual Integration of
Works-in-Progress
USER'S WORKING LEVELS
User Work-In-Progress Layers
User Work Products & Overlays Layer
Knowledge
Workers
WORKGROUP SHARED ASSETS
Requirement & Assumption Overlays
Immediate
CORPORATE KNOWLEDGE ASSETS
On-line
Latest Information Overlays
Information
Integration Overlays
Updates
Validated Workgroup Baseline
Aftermarket Overlays
Publishers Update Overlay
Published Reference Stack
Proprietary Knowledge Assets
Commercial
Knowledge
Providers
HIGH PERFORMANCE
KNOWLEDGE WEB
Seybold Enterprise Stack.dsf
27
BUILDER As Resource
Integrator to Unlimited Size
Stored in User's
Client Machine
Session
Directory
Dynamically Linked
SESSION OVERLAY
External
Relationship
File
Stored in Client
and/or
Fast (< 5 sec.)
Responding Server
Stack
Directory
External
Relationship
File
Compressed
Stack File
STACK
MASTER OVERLAY
Compressed
Stack Files
BUILDER stacks specific Virtual
Knowledge Layers located anywhere
on a Fast Responding network
Static or Dynamic
Layers & Overlays
Seybold Knowledge Integration.dsf
28
Constraint Browsers
understand choices & Outcomes
when there are no right answers
1
INFORMATION -matching Your situation
Current Situation
Experimenting
wiith this Choice
Modeled theory
as a system
of rational
Constraints
Expected Impacts
THEORY follows through
options to impacts
2
Desired
Outcome
UNEXPECTED
IMPACT
Sebold Knowledge Superiority.dsf
Understanding
Your Boss's Choices
Your Choices
Your Employee's Choices
29
Given an Age
Where Knowledge Need Never Die
What Do We Intend Now to Build
Make It Worthy of This Gift
30
Creating and Using
Systems That Know
- Anything August 2008
Chief 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 - 2008
3. 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
Formulating A Precise
Theory of Knowledge
Knowledge = Theory + Information
1987-1993
Knowledge As Evolutionary Science
Adapted from The Dragons of Eden , Carl Sagan, 1977
14
10
Humans
1013
Mammals
11
10
10
10
10 9
10 8
10
7
10
6
10
5
Reptiles
Genes vs Brains
Implying
Knowledge Type
From
Storage Type
10 4
10
3
10
2
10
1
Amphibians
Acquired Theory-based
Knowledge
1012
Jellyfish
Protozoa
Algae
Virus Bacteria
1
Instinctive DNA Inheritance
1
10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010
Evolutionary Biological Knowledge Types.dsf
SENSE ORGAN receipt of
Information produces
physiological
situation awareness
-- with or without a brain.
BRAINLESS animals react
using their instinctive dna
programs -- to succeed or die.
Badly adapted species die out.
Adapted from The Dragons of Eden , Carl Sagan, 1977
14
10
Humans
13
10
12
Mammals
11
10
10
10
10
9
10
8
Reptiles
Genes vs Brains
Implying
Knowledge Type
From
Storage Type
10 7
10
6
10
5
10
4
10
3
10
2
10
1
Amphibians
Knowledge
10
BRAIN memories model,
store, and teach
successful behaviors
as "lessons learned."
They constantly adapt
"brain content (Theory)"
with no need to change
the host's biological form.
Jellyfish
Protozoa
Algae
Virus Bacteria
1
1
10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010
Intelligent Animals Embrace Many "Behavior
Patterns" For Their Self-evident Success
KNOWLEDGE AGE
REQUIREMENTS
Survival
in Space
10 21
10 20
10 19
10 18
10 17
Modern
Civilization
Workgroups
10 16
10 15
10 14
Human
10 13
Mammals
Reptiles
10 12
Pentium
Laptop
HARDWARE
486 PC
386 PC
Amphibians
Jellyfish
Protozoa
Bacteria Algae
Virus
DOS
1
Knowledge
Codes &
Theory
Modeling
Gap
10 11
10 10
10 9
10 8
10 7
10 6
10 5
10 4
10 3
Turn Toward
Logic
SOFTWARE
+
Windows 98
Information
10 2
10 1
1
Life vs. Logic
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.
3. Badly matched to a problem,
their costs
CREATE "non-computability."
10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 1015 1016 1017 1018
Irrelevance of Logical Reasoning.dsf
Intelligent Animals Embrace Many "Behavior
Patterns" For Their Self-evident Success
KNOWLEDGE AGE
REQUIREMENTS
Survival
in Space
10 21
10 20
10 19
10 18
10 17
Modern
Civilization
Workgroups
10 16
10 15
10 14
Human
10 13
Mammals
Reptiles
10 12
Pentium
Laptop
HARDWARE
486 PC
386 PC
Amphibians
Jellyfish
Protozoa
Bacteria Algae
Virus
10 10
Knowledge
Codes &
Theory
Modeling
Gap
10 9
10 8
10 7
10 6
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.
10 5
10 4
10 3
Turn Toward
Logic
DOS
1
10 11
1. Extremely resource aware,
their many possible goals are
all Intentional, Competitive,
Success-oriented, and often
SOFTWARE
+
Windows 98
Information
10 2
10 1
1
10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 1015 1016 1017 1018
Conceptualizing and Organizing
All of Imagination and Reality
Mark 3
Top-Most
Primitives
Abstraction
Absolute
Being
Perfect
Intelligence
Imagination
Faith
Concepts
Form
Beauty
Design
Theology -- Random House Dictionary
Pure Intellect
(Hypthetical
Models)
Aggregation
"collection of particulars
into a whole mass or sum"
Physical
Universe
-- Random House Dictionary
Ontology
Ontological Primitives
Natural Laws
Local Groups
Epistemology
Axiomologies
Galaxies
Cosmology
Planets
Practical
Rationalism
Truth
Logic
Mathematics
Requirements
Ideas
Categories
"the act of considering
something as a general
quality or characteristic,
apart from concrete
realities, specific objects,
or actual instances"
(Acquired Models)
Societies
Sociobiology
Animals
Psychobiology
Machines
Theories
Models
Everything
Imaginable
Every "Science"
Every "Fantasy"
Imagination & Reality.dsf
Biology
Formalisms
Chemistry
Matter
Energy
SKELETAL CIRCULATORY
DIGESTIVE NERVOUS
Particles
Guanine
Phenomena
Events
Observables
Sense Data
Everything Real &
Observable
Knowledge Theoretic
Representations of Thought
Pure Intellect
Absolute Theology
Ontology
Being
Rationalism
Categories
Perfect Forms
Intelligence
Concepts
PREDICTIVE WEB
Truth
Universals
Logic
Measured by Theory
Knowledge Theoretic Representation.dsf
DECISION IMPACT
Beauty
P( ... x, y, z)
×
Models
P(a, b, c ,... | ... x, y, z)
Epistemology
P(a, b, c ,... x, y, z)
THEORY-BASED
3
MEDIATING
STRUCTURE
Probability of
Recognizing
Situation
Correctly
n>2
X
n=2
a
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SITUATION
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Ph
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Knowing
Current or
Hypothetical
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1
Probability
of Predicting
Outcomes for
Every Choice
ENTITY
Probability of
Knowing Every
Option Outcome
Before Decisions
Are Made
"Knowledge
Theory-based
Semantic Web"
ys
ic
Formal
Languages
LANGUAGE
Situation Constrained
Navigation of Every
Accepted Fact, Theory, &
Predictable Decision Impact
s
Natural
Chemistry
Languages
Biology
Exemplars
Psychobiology
Sociobiology
Abstract
ART
Realistic
Cosmology
Empiricism
Sense Data
Icons
Photographs
Planets
Animals
Machines
Matter Materials
Phenomena
SYMBOLS
& CODES Energy
Mathematics
Measurement
Persistent
Representations
of Knowledge
Physical
Universe
Particulars
Observed Reality
Replace Language
s
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s
hy
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Metae
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Physical
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Persistent
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s
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In All Languages
Physical
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semantic webs there
only once in any semantic web, its unique
code locates it instantly -- without search
Model Instance Codes.dsf
Probabilistic "Practical Rationality"
Physical Theory of Knowledge & Computation
"Information, Structure, Inference
-- A Physical Theory of Knowledge and Computation"
Dr. Richard L. Ballard, 1993
Probability of
Knowing Every
Option Outcome
Before Decisions
Are Made
DECISION IMPACT
Probability
of Predicting
Outcomes for
Every Choice
Knowing
Current or
Hypothetical
Situation
PREDICTIVE WEB
X
Probability of
Recognizing
Situation
Correctly
SITUATION
P(a, b, c ,... x, y, z) = P(a, b, c ,... | ... x, y, z) × P( ... x, y, z)
Theory-based
Semantic
Web
Reality
Physical Event of
"Thought" or
"Execution"
Probabilistic Knowledge Theory A.dsf
Probability of
Knowing Every
Option Outcome
Before Decisions
Are Made
Probability
of Predicting
Outcomes for
Every Choice
DECISION IMPACT
Knowing
Current or
Hypothetical
Situation
PREDICTIVE WEB
X
Probability of
Recognizing
Situation
Correctly
SITUATION
Theory-based
Degrees of Freedom & Constraint
a' priori
a, b, c, ...
a' posteriori
... x, y, z
Goals
Time
Education
Relation
Responsibility
Resource
Requirements Opportunity
Intent
Action
Semantic
Web
Reality
Physical Event of
"Thought" or
"Execution"
Knowledge As A
Quantitative Hard Science
DECISION IMPACT
Theory-based
Reality
PREDICTIVE WEB
SITUATION
Semantic Web
Fundamental Ultimate Limit Measures
KNOWLEDGE
=
Decision Success P(task)
Quantitative Hard Science.dsf
THEORY
a' priori
a, b, c, ...
+ INFORMATION
a' posteriori ... x, y, z
Copyright Knowledge Foundations 2006
Knowledge As A
DECISION IMPACT
Theory-based
Reality
PREDICTIVE WEB
SITUATION
Semantic Web
KNOWLEDGE
=
Knowledge Theory-based
Ultimate Minimum
Decision Resource Cost
THEORY
+ INFORMATION
Ballard
Shannon
Education, Web Certification, Information Bandwidth
& Theory Capture Limit Cost
& Storage Limit Cost
-log{P(a, b, c,...x, y, z)} -log{P(a, b, c,...|....x, y, z)}
-log{P(...x, y, z)}
Theory links task specific
Theory provides performancesuccesses to most effective based measures comparing
trade-offs in training, theory Education, Theory Capture, &
creation, & technology use Knowledge Creation investment
Theory predicts that
costs can & will scale
proportionally to
Information Content
a' priori
a, b, c, ...
a' posteriori ... x, y, z
On Creating A Third
Generation
Knowledge Tool
Advanced Engineering 1993 - 2004
Breakthroughs 2005 - 2008
MARK 3 ALPHA BACKEND
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FUNDAMENTAL DEFINITIONS
in
Knowledge Science & Engineering
December 2004
Copyr ight Richa rd L. Ballar d 19 94-2 006
EditForms
Translating Knowledge into
Patterns of Thought
FUTURE ENTERPRISE & INVENTION
CODING MOST SECRET
Left
Secondary
Option
Right
Secondary
Option
KNOWLEDGE FOUNDATIONS
MARK 3 VERSION 1 ALPHA
BROWSER Fact Sheets
FIRST COURSE IN KNOWL EDGE ENGIN EERING
Cre ating Systems That Kno w
CRE ATING A NEW SC IE NCE 1970-2005
Left - most
Secondar y
Option
Centered
Primary
Option
COPYRIGHT
COPYRIGHT
February 2007 - September 2007
SE
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Architecture of
Concept Knowledge Flow
Foundations
DLL
Foundations
EXE
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Base Class
OPEN MFC
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FOUNDATIONS
KB LAYER 1
SOURCES
DOC2
DOC3
EDIT STACK
EDITOR1
EDITOR2
EDITOR3
ONTOLOGY OVERLAY
DEV HISTORY OVERLAY
KB LAYER 2
KB LAYER 3
KB STACK A
KB STACK B
DEVTOOLS
USERTOOLS
COMMON FILES
WORKSPACES
USER1
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CONCEPT
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Mk 3 Client Browser/Finder
BROWSER
Architectures
Mark 3 vs Conventional
• Conventional Layer Cake
• KFI - Mark 3
Theory-based Knowledge Integration Code, Structure & Object Integration
Databases
Operating System
©Knowledge Foundations, Inc./D.L. Thomas
Open source diagram
NEED TO MATCH CONCEPTS ACROSS
ALL KNOWLEDGE SOURCES
International
Organizations
Allied
Governments
Military
Facilities
Intelligence
Organizations
Government
Organization
Threat
Geography
Threat
Scenarios
Operational
Forces
Navy
Organization
DoD
Organization
Threat
Databases
Mission
Task Analysis
Training
Requirements
Crew Centered
WarfareINDUSTRY
STANDARD
Requirements
Requirements
Operational
Concepts
Mission
Requirements
Training
Objectives
Integrate Knowledge
Training
Design
Produced By
Facilities
Model Base
ALL PUBLISHERS
Mission
Technology
Simulation
Requirements
USER'S WORKING LEVELS
Air Force
Organization
R&D
User Work-In-Progress Algorithmic
Layers
Technology
Programs
Model
Base
Testbeds
User Work Products & Overlays
Layer
Technology
Procurment
System
Congressional
WORKGROUP
SHARED ASSETS
Integration
Programs
Simulation
Committees
Model BaseRequirement & Assumption Overlays
System
Political &
Performance
Major Defense
Major Weapon
Performance
Economic
Analysis
Contractors
Systems
Requirements
Base
ModelASSETS
Base
Cost &
CORPORATE KNOWLEDGE
System
Technology
Major
Manufacturing
TestOverlays
Risk
SubsystemsLatest Information
Associations
Facilities
Modeling
Integration
System Overlays
Major
Manufacturing
Test
Aftermarket
Overlays
Subcontractors
Technology
Copyright
MARK 2 BUILDER
Requirements
Congress
KNOWLEDGE RESEARCH
1990-2005
Patterns of Thought
1990-2001
Theory-based
Semantics
Industry-wide Asset Integration.dsf
Mark 3, Version 2: Design Objective
To Begin The Process Of Assessing
The Impact Of Every Situation
3 4
...International
x, y, z
SITUATION
P( ... x, y, z)
Threat
Training
Threat
Objectives
Databases
8 7 Geography
6 5
Allied
Mission
Military
Threat
Training
Governments
Task Analysis
Facilities 10 11
Scenarios
Requirements
9
Crew Centered
Intelligence
Warfare
Operational
Requirements
Organizations
Requirements
Forces
1
Organizations
13 12 Operational
Navy
Organization
Concepts
Government
Organization
2
DoD
Organization
Mission
Requirements
2
19
Match ing
NAT O Jammer
Band
18
17
16
Rad ar?
15
15
Just th e
Bad Gu ys
14
To p Gu n
13
4
Model: COUNTRIES
... x, y, z
Instance 7:
Republic of Iraq
Rel. Model:
COUNTRY/MILITARY
FACILITIES
Lo cating
Rel. Instance 3:
Country/Military
Facilities
3
6
Model:
MILITARY AIRFIELDS
7
Instance 3:
Balad Airfield
3
Rel. Model:
FACITITIES/SUBCOMPONENTS
Tar get
8
Rel. Instance 2:
Facitities/SubComponents
2
Wha t is Th er e?
12
Armo red,
Sh oo ts bigger ,
farther &
faster than us
Can he see us?
Who
else?
Countries
5
Patterns of Thought
1990-2001
KNOWLEDGE RESEARCH
1990-2005
Tak es ou t
J-5 too
3
7
R&D
Algorithmic
Technology
Programs
Model Base
Testbeds
Technology
Procurment
System
Congressional
Integration
Programs
Simulation
14
Committees
18
Model Base
System
Political &
Performance
Major Defense
Major Weapon
Performance
Economic
Analysis
Contractors
Systems 15 16 17Requirements
Base
Model Base
Cost &
System
20 19
Technology 24 23
Major
Manufacturing
Test
Risk
Subsystems
Associations
Facilities
Modeling
System
Major
Manufacturing 22 21
Test
Subcontractors
Technology
MARK 2 BUILDER
Copyright
Requirements
20
Office of the
Secretary of Defense
N=3
ASSUMPTIONS:
Sit ua ti on
Mission
Simulation
Air Force
Organization
Congress
ACE CVN-77
Program Management
Knowledge Base 1998
Training
Facilities
Design
Model Base
Technology
Requirements
SITUATION
1
11
Hea rt o f
Da rkness
10
9
Wh o is sho oting
at Us?
Nig htmar e, Wh en
F lying lo w!
Wh ere is his
weak ness?
Wha t
frequ enc ies?
Or ga nization
& Eq uipment
Who h as g ot o ne?
5
5
8
3
4
2
5
9
Model:
Rel:
Model:
Rel:
Rel:
Model:
SYS/BAND JAM BAND JAM/FREQ CHANNELS SOURCE/FREQ FIRE CONTROL RADARS Model:
Instance 5:
Instance 5: Instance 3:
Instance 8:
Instance 2:
Instance 4: AAA GUNS
Pod/Freq ALQ-99 Band 10 Jam/Freq
21
Air For ce
23
22
Na vy
Channel J-6
Source/Freq Gun Dish Fire Control Radar
Wha t else
do you ca rry?
24
I wa nt this guy
with u s!
Mou nt the
band 10
jammer po d!
Model:
JAMMER SYSTEM
Instance 1: ALQ-99F
2
5
Model:
EA-6B WEAPONS
Instance 5: Jammer Loadout
2
Model:
EA-6B Prowler
Instance 2:
Rel Model: SYSTEM/SUB-SYSTEM
Instance 2: System/Sub-Sys
Semantic Web 1.dsf
5
Instance 9:
ZSU-23 Gun
4
2
1
Rel Instance 5:
Organization /
Equipment
8
4
Model:
Model:
Model: SURFACE
AAA PLATOONS AAA BATTERIES AIR DEFENSES
Instance 4:
ZSU-23 Platoon
Instance 2:
ZSU-23 Battery
Rel. Model: TABLE OF ORGANIZATION & EQUIPMENT
Who c arries
this system?
1
2
Rel Instance 2:
Organization /
Equipment
Rel Instance 1:
Organization /
Equipment
Instance 8:
Balad Surface
Air Defenses
Rel Instance 4:
The Answer to Any Question is
the Whole "Chain of Reasoning"
To Assess Every Possible Option
And Decision Impact
3 4
...International
x, y, z
Training
Threat
SITUATION
Objectives
Databases
P( ... x,
y, z)
Mission
Threat
Training
Threat
Geography
Organizations
8 7
6 5
Military
Task Analysis
Facilities 10 11
Scenarios
Requirements
9
Crew
Centered
Intelligence
Warfare
Operational
Requirements
Organizations
Requirements
Forces
1
Allied
Governments
Organization
2
DoD
Mission
Organization
Requirements
R&D
Algorithmic
Technology
Programs
Model Base
Testbeds
Technology
Procurment
System
Congressional
Integration
Programs
Simulation
14
Committees
18
Model Base
System
Political &
Performance
Major Defense
Major Weapon
Performance
Economic
Analysis
Contractors
Base
Model Base
Cost &
24
23
System
20 19
Technology
Major
Manufacturing
Test
Risk
Subsystems
Associations
Facilities
Modeling
System
22
21
Major
Manufacturing
Test
Subcontractors
Technology
MARK 2 BUILDER
Copyright
Requirements
20
19
Match ing
NAT O Jammer
Band
18
17
16
Rad ar?
15
15
Just th e
Bad Gu ys
To p Gu n
14
13
Armo red,
Sh oo ts bigger ,
farther &
faster than us
Can he see us?
Tak es ou t
J-5 too
Who
else?
Countries
4
Model: COUNTRIES
Instance 7:
Republic of Iraq
Rel. Model:
COUNTRY/MILITARY
FACILITIES
5
Lo cating
Rel. Instance 3:
Country/Military
Facilities
3
6
Model:
MILITARY AIRFIELDS
7
Instance 3:
Balad Airfield
3
Tar get
Rel. Model:
8
Rel. Instance 2:
2
Patterns of Thought
1990-2001
KNOWLEDGE RESEARCH
1990-2005
3
7
Air Force
Organization
Congress
Office of the
Wha t is Th er e?
12
11
Hea rt o f
Da rkness
10
Or ga nization
& Eq uipment
5
4
5
2
5
8
3
1
4
9
4
2
2
8
Model:
Model:
Rel:
Model:
Rel:
Rel:
Model:
Model:
Model:
Model:
SURFACE
JAM
BAND
JAM/FREQ
CHANNELS
SOURCE/FREQ
FIRE
CONTROL
RADARS
SYS/BAND
Instance 5:
Instance 8:
Instance 2:
Instance 4: AAA GUNS AAA PLATOONS AAA BATTERIES AIR DEFENSES
21
Air For ce
Channel J-6
23
22
Na vy
Wha t else
do you ca rry?
24
I wa nt this guy
with u s!
Mou nt the
band 10
jammer po d!
Instance 1: ALQ-99F
2
5
Model:
EA-6B WEAPONS
2
Model:
EA-6B Prowler
Instance 2:
Semantic Web 2.dsf
5
Instance 4:
ZSU-23 Platoon
Instance 2:
ZSU-23 Battery
Who c arries
this system?
1
Model:
JAMMER SYSTEM
Instance 9:
ZSU-23 Gun
Rel Instance 5:
Organization /
Equipment
... x, y, z
Rel Instance 2:
Organization /
Equipment
Rel Instance 1:
Organization /
Equipment
THEORY
N=7
Decisive
degrees
of
freedom
CONOPS
Option #1
9
Wh o is sho oting
at Us?
Nig htmar e, Wh en
F lying lo w!
Wh ere is his
weak ness?
Wha t
frequ enc ies?
Who h as g ot o ne?
N=3
ASSUMPTIONS:
Sit ua ti on
Mission
Simulation
Technology
Requirements
ACE CVN-77
Program Management
Knowledge Base 1998
2
PREDICTIVE WEB
Training
Operational
b,Design
cBase
,... | ... x,Facilities
y, z)
Concepts P(a, Model
13 12
Navy
Government
Organization
SITUATION
1
N=4
DECISIONS:
Instance 8:
Balad Surface
Air Defenses
Rel Instance 4:
On Achieving
Ballard / Shannon
Limit Success
Ability to Store Unlimited Knowledge
In Absolute Minimum Space
Mainframe, Blade Servers & Software
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).
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
Employs
Constraint 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 diagnostics
for 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.
Foundations Browser -- [Medical Guide]
File
View
Window
Help
Concept:
Constraint Browser
Views: Constraint
Time
No Correlations
Known
Cluster
Concept
Weak Correlation's
Neglected
Flat Dark Red
Spots, Do
Not Fade
Dull Red
Splotches,
Do Fade
Light Red
Widespread Rash Spreads Bright Red
Ichy, Blistery from Central Rash Affecting Rash on
Trunk or Face
Cheeks
Red Spot
Rash
Severe
Headache
Remaining Strong
Correlations
Parameters
Chosen
Mild or
No
Headache
None
Above
Sore
Throat
Continue
Prescription
Rubella
Pneumonia
Indication #1
RASH
NoRash
Rash
Above
100 F (38 C)
Temperature
NO
Fever
Temperature
Emergency
Urgent
Drug Alergy
Thrombocytopenia
Help
Bring Down
Fever
Call
Doctor in
24 hours
Medical
Help
Measles
Scarlet
Fever
Chicken
Pox
Self-Help
Bring Down
Fever
Self-Help
Home
Pregnancy
Urgency
Meningitus
Lyme
Disease
Parvovirus
Acute
Bronchitis
Diagnosis
.1
.2 .3 .4 .5
.2 .3 .4 .5
1 hr.
1 day
.2 .3 .4 .5
1 week .2 .3 .4 .5
1 month .2 .3 .4 .5
1 year 2
3 4 5
10 year 20 30 40 50
100
Time
Start by choosing your costs first.
?
?
Not Feeling Well
Control Dashboard
SELECTED CONCEPT
Diagnosis -Not Feeling Well
Start
Medical Guide
Foundations BROWSER
RELATIONSHIPS
Diagnostics 3-13
PATH NAMES
Type to search
11:58 PM
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
Concept:
Constraint Browser
Views: Constraint
Cluster
Time
No Correlations
Known
Concept
Weak Correlation's
Neglected
Flat Dark Red
Spots, Do
Not Fade
Remaining Strong
Correlations
Parameters
Chosen
Severe
Headache
Indication #1
RASH
NoRash
Rash
Above
100 F (38 C)
Temperature
Temperature
Emergency
Urgency
Meningitus
Diagnosis
.1
.2 .3 .4 .5
1 hr.
.2 .3 .4 .5
1 day
Time
?
?
Not Feeling Well
?
Control Dashboard
Start
SELECTED CONCEPT
RELATIONSHIPS
Emergency
Diagnostics 7
Medical Guide
Foundations BROWSER
PATH NAMES
Type to search
11:58 PM
Selecting Parameters Chosen accepts all those 4 rows of conditions assumed.
Then it chooses to look higher at the less significant symptoms.
File View
Window Help
Concept:
Constraint Browser
Views: Constraint
Cluster
Time
No Correlations
Known
Concept
Weak Correlation's
Neglected
Flat Dark Red
Spots, Do
Not Fade
Remaining Strong
Correlations
Parameters
Chosen
Severe
Headache
Indication #1
RASH
NoRash
Rash
Above
100 F (38 C)
Temperature
Temperature
Emergency
Urgency
Meningitus
Diagnosis
.1
.2 .3 .4 .5
1 hr. .2 .3 .4 .5
1 day
Time
?
?
Not Feeling Well
?
Control Dashboard
Start
SELECTED CONCEPT
RELATIONSHIPS
Emergency
Diagnostics 7
Medical Guide
Foundations BROWSER
PARAMETERS CHOSEN
Temperature -- Above 100F
Urgency -- Emergency
Diagnosis -- Meningitus
Time -- 6 min -> 1 day
PATH NAMES
Type to search
11:58 PM
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
Concept:
Constraint Browser
Views: Constraint
Time
No Correlations
Known
Cluster
Concept
Weak Correlation's
Neglected
Remaining Strong
Correlations
Parameters
Chosen
Drowsiness Severe Dislike Pain Bending Nausea or
& Confusion Bright Lights Head Forward Vomiting
Indication #2
Flat Dark Red
Spots, Do
Not Fade
Severe
Headache
Indication #1
RASH
NoRash
Rash
?
?
Not Feeling Well
?
Control Dashboard
Start
SELECTED CONCEPT
RELATIONSHIPS
Emergency
Diagnostics 7
Medical Guide
Foundations BROWSER
PARAMETERS CHOSEN
Temperature -- Above 100F
Urgency -- Emergency
Diagnosis -- Meningitus
Time -- 6 min -> 1 day
PATH NAMES
Type to search
11:58 PM
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.
File
View
Window
Help
Concept:
Constraint Browser
Views: Constraint
Time
No Correlations
Known
Cluster
Concept
Weak Correlation's
Neglected
Flat Dark Red
Spots, Do
Not Fade
Severe
Headache
Mild
Headache
Remaining Strong
Correlations
Parameters
Chosen
No
Headache
Indication #1
RASH
NoRash
Rash
Above
100 F (38 C)
Temperature
Temperature
Urgent
Urgency
Drug Alergy
Thrombocytopenia
Pneumonia
Diagnosis
.1
.2 .3 .4 .5
1 hr. .2 .3 .4 .5
1 day
.2 .3 .4 .5
1 week .2
Time
?
?
Not Feeling Well
Control Dashboard
SELECTED CONCEPT
Diagnostics 7- 9
Urgent
Start
Medical Guide
RELATIONSHIPS
Foundations BROWSER
PATH NAMES
Type to search
11:58 PM
Once a diagnosis is determined, users can pursue treatment options
at "the speed of thought."
Knowledge of every possibility is immediately available to every potential patient.
File
View
Window
Help
Concept:
Constraint Browser
Views: Constraint
Time
No Correlations
Known
Cluster
Concept
Weak Correlation's
Neglected
Flat Dark Red
Spots, Do
Not Fade
Dull Red
Splotches,
Do Fade
Light Red
Widespread Rash Spreads Bright Red
Ichy, Blistery from Central Rash Affecting Rash on
Trunk or Face
Rash
Red Spot
Cheeks
Severe
Headache
Remaining Strong
Correlations
Parameters
Chosen
Mild or
No
Headache
None
Above
Sore
Throat
Continue
Prescription
Rubella
Pneumonia
Indication #1
RASH
NoRash
Rash
Above
100 F (38 C)
Temperature
NO
Fever
Temperature
Emergency
Urgent
Help
Bring Down
Fever
Call
Doctor in
24 hours
Medical
Help
Self-Help
Bring Down
Fever
Self-Help
Home
Pregnancy
Urgency
Meningitus
Drug Alergy
Thrombocytopenia
Measles
Scarlet
Fever
Chicken
Pox
Lyme
Disease
Parvovirus
Acute
Bronchitis
Diagnosis
.1
.2 .3 .4 .5
1 hr. .2 .3 .4 .5
1 day .2 .3 .4 .5
1 week .2 .3 .4 .5
1 month .2 .3 .4 .5
1 year 2
3 4 5
10 year 20 30 4050
100
Time
?
?
Not Feeling Well
Control Dashboard
SELECTED CONCEPT
Start
Medical Guide
Foundations BROWSER
RELATIONSHIPS
Diagnostics 3-13
PATH NAMES
Type to search
11:58 PM
Presenter:
Chief Scientist
Systems That Know -ULTIMATE INNOVATIONS
? ?
? ?
? ?
?
? ?? ?
? ? ? ?
? ?
?
N-Dimensional
SITUATION AWARENESS
Predicts Theory-based
DECISION OPTION
CONSTRAINT TRADE-OFFS
N = 8"z"
"y"
"x"
"q"
"r"
"s"
"t"
"u"
"v"
"w"
N=7
Semantic Web
"Chain of Reasoning"
N-ary
THEORY-BASED
RELATIONSHIP
BUNDLE
Knowledge
FOUNDATIONS
www.KnowledgeFoundations.com
1
Presenter:
Chief Scientist
2
KNOWLEDGE
MANAGEMENT
The Intentional and Competitive Human Endeavor
To Make Things Happen That Do Not Happen By Themselves
Embraces
KNOWLEDGE SCIENCE
Testable Natural Sciences, Capable of Learning and Changing,
Pursuing Know ledge Advancem ents Toward
M ost Elegant Sim plicity and Ultim ate Lim it Efficiency
Copyright Dr. Richard L. Ballard 2006
KM Embraces KS.dsf
3
Most Revolutionary
Time Imaginable
TECHNICALLY and IN PRINCIPLE:
Every organization can go out today and
Buy a desktop computer with the capacity
To store All The World's Knowledge.
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
STK - Objectives 1.dsf
4
Questions About "Knowledge"
Are Always Deeper Than
CPU MACHINES
We Want To Go
Conventional Languages
Logic & Mathematics
Certain Truth
Object Orientation
Relational Databases
Information Theory
Unchanging Standards
LOGICAL / LINGUISTIC /
PROCEDURAL Standard
EXPERIMENTAL / CONCEPTUAL /
DECLARATIVE Standard
Coded Language of Thought
Experimental Natural Sciences
Uncertain Probability
Conceptualism
Semantic Networks
Knowledge Theory
Evolutionary Standards
CPU & MEMORY MACHINES
One Standard for Humans, One for Machines, or
KNOWLEDGE?
?
One Standard Integrating ALL KNOWLEDGE
Which One Can Really Do That?
STK - Objectives 2.dsf
Coptright Dr. Richard L. Ballard 2006
5
Adapted from The Dragon's of Eden, Carl Sagan, 1977
14
10
Humans
13
10
Mammals
11
10
10
10
10
9
10
8
Reptiles
Genes vs Brains
Implying
Knowledge Type
From
Storage Type
10 7
10 6
10
5
10
4
Amphibians
10 3
10 2
10
Knowledge
1012
Jellyfish
Protozoa
Algae
Virus Bacteria
1
1
1
10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010
BRAIN memories model,
store, and teach
successsful behaviors
as "lessons learned,"
constantly adapting
"brain content (Theory
(Theory)
Theory)"
with no need to change
their host's biological form
SENSE ORGAN receipt of
Information produces
physiological
situation awareness
-- with or without a brain.
BRAINLESS animals react
only from instinctive dna
programs -- to succeed or die.
poory adapted species die out.
6
Intelligent Animals Embrace Many Behavior
Patterns For Their Self-evident Success
KNOWLEDGE AGE
REQUIREMENTS
Survival
in Space
Extremely Resource Aware,
Their Many Alternative Goals
Are Intentional, Competitive,
Success-oriented
Success-oriented,
oriented, and Often
Achievable in Multiple Ways.
10 21
10 20
10 19
10 18
10 17
Modern
Civilization
Workgroups
10 16
10 15
10 14
Human
10 13
Mammals
Reptiles
10 12
Pentium
Laptop
HARDWARE
486 PC
386 PC
Amphibians
Jellyfish
Protozoa
Bacteria Algae
Virus
10 10
Knowledge
Codes &
Theory
Modeling
Gap
10 9
10 8
SOFTWARE
+
Windows 98
Information
They assume most choices are
not provably right or wrong,
seek to enumerate all options,
and predict the consequences
of each option before deciding.
10 7
10 6
10 5
10 4
10 3
Turn Toward
Logic
DOS
1
10 11
They reject options that do
not match their situation or
trust..
go against theories they trust
10 2
10 1
1
10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 1010 1011 1012 1013 1014 1015 1016 1017 1018
Logical Self-consistency
insures machine-like behaviors
guaranteed to follow external
mandates, e.g. common grammar
Possess no intrinsic awareness
of efficiency, resource usage, or
the complexity of methodologies
-- a source of non-computability,
when badly matched to problem.
7
"As far as the propositions
of mathematics refer to reality,
they are not certain;
and
As far as they are certain,
they do not refer to reality."
Albert Einstein -- Ideas and Opinions, 1988
Nobel Prize in Physics 1921
So while
guaranteed self-consistency is the virtue of logic,
guaranteed uncertainty is the accepted fact of
experimental quantum physics.
Which truth should our new science choose?
Einstein Physics Quote.dsf
8
Ultimate Innovations
These are the inventions or discoveries that operate
at the
Ultimate Natural Limits of Reality
To capture
Every Form of Knowledge is not about
dealing with infinite complexies,
Nature is everywhere finite.
Successful representations of knowledge seek to find
Nature's Most Elegant Limit Simplicities.
ARGUABLY,
These Leave No Room for Technical Improvement
Ultimate Innovations.dsf
9
Conceptualizing and Organizing
All of Imagination and Reality
Mark 3
Top-Most
Primitives
Abstraction
Absolute
Being
Perfect
Intelligence
Imagination
Faith
Concepts
Form
Axiomologies
Beauty
Design
Theology
Pure Intellect
(Hypthetical
Models)
Aggregation
"collection of particulars
into a whole mass or sum"
Physical
Universe
Ontology
Ontological Primitives
Natural Laws
Local Groups
Galaxies
Epistemology
Cosmology
Planets
Practical
Rationalism
Truth
Logic
Mathematics
Requirements
Ideas
Categories
"the act of considering
something as a general
quality or characteristic,
apart from concrete
realities, specific objects,
or actual instances"
(Acquired Models)
Societies
Sociobiology
Animals
Psychobiology
Machines
Theories
Models
Everything
Imaginable
Every "Science"
Every "Fantasy"
Biology
Formalisms
Chemistry
Matter
Energy
SKELETAL CIRCULATORY
DIGESTIVE NERVOUS
Particles
Guanine
Phenomena
Events
Observables
Sense Data
Everything Real &
Observable
Imagination & Reality.dsf
10
Knowledge Theoretic
Representations of Thought
Absolute Theology
Ontology
Being
Rationalism
Categories
Perfect Forms
Intelligence
Concepts
Truth
Universals
Logic
Measured by Theory
Knowledge Theoretic Representation.dsf
DECISION IMPACT
Beauty
P( ... x, y, z)
×
P(a, b, c ,... | ... x, y, z)
Pure Intellect
PREDICTIVE WEB
Models
P(a, b, c ,... x, y, z)
Epistemology
Probability of
Recognizing
Situation
Correctly
THEORY-BASED
3
MEDIATING
STRUCTURE
X
n>2
y
SITUATION
n=2
ph
a
et
M
s
Knowing
Current or
Hypothetical
Situation
N-ARY
RELATIONSHIP 2
c
si
Probability
of Predicting
Outcomes for
Every Choice
1
ENTITY
Probability of
Knowing Every
Option Outcome
Before Decisions
Are Made
"Knowledge
Theory-based
Semantic Web"
Ph
ys
Formal
ic
Languages
s
Situation Constrained
Navigation of Every
Accepted Fact, Theory, &
Predictable Decision Impact
LANGUAGE
Natural
Chemistry
Languages
Biology
Exemplars
Psychobiology
Sociobiology
Abstract
ART
Realistic
Cosmology
Empiricism
Sense Data
Icons
Photographs
Planets
Animals
Machines
Matter Materials
Phenomena
SYMBOLS
& CODES Energy
Mathematics
Measurement
Physical
Universe
Particulars
Observed Reality
Persistent
Representations
of Knowledge
11
Replace Language
M
e
ta
ph
Model
Instance 0
Absolute
Being
DataType
Model
Perfect Instance 0
Intelligence
Model
Instance 0
i
ys
cs
Ph
ys
MetaPhysical
Concept
I
n
s
t
a 1-N
n
c
e
s
DataForm
Concept
I
n
s
t
a 1-N
n
c
e
s
Physical
Concept
Persistent
Representations
of Knowledge
ic
s
Are Unique and Identical
In All Languages
Physical
Universe
I
n
s
t
a 1-N
n
c
e
s
semantic webs there
only once in any semantic web, its unique
code locates it instantly -- without search
Model Instance Codes.dsf
12
1970-1993
NSF Science Education
Quantum Simulations &
"Star Wars" Battle Management
Produce Ultimate Limit
Theory For Knowledge and Computation
Ballard (1987-1993)
Defines First Declarative
Theory-Based Semantic Web
Predicting All Decison Impacts
Before Choices Are Made
First Semantic Web.dsf
Copyright Knowledge Foundations Inc. 2006
13
1993 - 2006
BREAKTHROUGH IMPORTANCE
of Possessing Only Comprehensive
Natural Science Theory-based
THEORY OF KNOWLEDGE
AND COMPUTATION
Brain-based
Intelligence Is Empirically Predictive,
-- Not Merely Categorically Reactive
Ontological Object Orientation and
Logical Inference Simply React
Most Essential Breakthrough.dsf
14
Probability of
Knowing Every
Option Outcome
Before Decisions
Are Made
Probability
of Predicting
Outcomes for
Every Choice
DECISION IMPACT
Knowing
Current or
Hypothetical
Situation
PREDICTIVE WEB
X
Probability of
Recognizing
Situation
Correctly
SITUATION
Theory-based
Degrees of Freedom & Constraint
a' priori
a, b, c, ...
a' posteriori
... x, y, z
Semantic
Web
Goals
Time
Education
Relation
Responsibility
Resource
Requirements Opportunity
Intent
Action
Reality
Physical Event of
"Thought" or
"Execution"
15
Predicting and Assessing
Decision Impacts SITUATION
3 4
...International
x, y, z
Organizations
SITUATION
P( ... x,
y, z)
Mission
Threat
Threat
Geography
8 7
1
6 5
Military
Training
Task Analysis
Facilities 10 11
Scenarios
Requirements
9
Crew Centered
Intelligence
Warfare
Operational
Requirements
Organizations
Requirements
Forces
1
Allied
Governments
Navy
Government
Organization
2
PREDICTIVE WEB
Operational
Training
Design
cBase
,... | ... x,Facilities
y, z)
Concepts P(a, b,
Model
13 12
Organization
2
DoD
Mission
Organization
Requirements
20
19
Match
Matching
ing
Jammer
NAT
NATO
OJammer
Band
Band
18
17
16
Radar?
ar?
Rad
15
15
Just th
thee
Just
Bad Gu
Guys
ys
Bad
ToppGu
Gun
n
To
14
13
4
Model: COUNTRIES
Instance 7:
Republic of Iraq
Rel. Model:
COUNTRY/MILITARY
FACILITIES
Lo
Locating
cating
Rel. Instance 3:
Country/Military
Facilities
3
6
Model:
MILITARY AIRFIELDS
7
Instance 3:
Balad Airfield
3
Rel. Model:
Target
get
Tar
8
Rel. Instance 2:
2
Wha
Whatt is
is Th
There?
ere?
12
Armo
Armored,
red,
Shoo
Sh
oots
ts bigger
bigger,,
farther &
farther
&
faster than
than us
faster
us
Can
Can he
he see
see us?
us?
Who
Who
else?
else?
Countries
5
Patterns of Thought
1990-2001
Tak
Takes
es ou
outt
J-5 too
too
J-5
3
7
R&D
Algorithmic
Technology
Programs
Model Base
Testbeds
Technology
Procurment
System
Congressional
Integration
Programs
Simulation
14
Committees
18
Model Base
System
Political &
Performance
Major Defense
Major Weapon
Performance
Economic
Analysis
Contractors
Base
Model Base
Cost &
System
20 19
Technology 24 23
Major
Manufacturing
Test
Risk
Subsystems
Associations
Facilities
Modeling
System
Major
Manufacturing 22 21
Test
Subcontractors
Technology
MARK 2 BUILDER
Copyright
Requirements
KNOWLEDGE RESEARCH
1990-2005
Office of the
11
Hea
Heart
rt o
off
Darkness
rkness
Da
10
Or
Orga
ganization
nization
& Eq
Equipment
uipment
&
5
4
8
5
3
2
5
4
9
4
2
2
1
8
Model:
Rel:
Model:
Model:
Rel:
Rel:
Model:
Model:
Model: SURFACE
SYS/BAND JAM BAND JAM/FREQ CHANNELS SOURCE/FREQ FIRE CONTROL RADARS Model:
Instance 5:
AIR DEFENSES
Instance 8:
Instance 2:
Instance 4: AAA GUNS AAA PLATOONS AAA BATTERIES
21
Air For
Force
ce
Air
23
22
Na
Navy
vy
Channel J-6
Whatt else
else
Wha
do you
you ca
carry?
rry?
do
24
want
nt this
this guy
II wa
guy
with u
with
us!
s!
Mou
Mount
nt the
the
band 10
10
band
jammer
jammer po
pod!
d!
1
Model:
JAMMER SYSTEM
Instance 1: ALQ-99F
2
Model:
EA-6B WEAPONS
2
Model:
EA-6B Prowler
Instance 2:
Semantic Web.dsf
5
5
Instance 9:
ZSU-23 Gun
Instance 4:
ZSU-23 Platoon
Instance 2:
ZSU-23 Battery
Who c
carries
arries
Who
this system?
system?
this
Rel Instance 5:
Organization /
Equipment
... x, y, z
Rel Instance 2:
Organization /
Equipment
Rel Instance 1:
Organization /
Equipment
THEORY
N=7
Decisive
degrees
of
freedom
CONOPS
Option #1
9
Wh
Who
o is
is sho
shooting
oting
at
at Us?
Us?
htmare,
e,Wh
When
en
Nig
Nightmar
low!
w!
F
Flying
lying lo
Where
ere is
is h
his
is
Wh
weakne
ness?
ss?
weak
Wha
Whatt
ies?
frequ
frequenc
encies?
Who h
Who
has
as got
got o
one?
ne?
N=3
ASSUMPTIONS:
uati
tion
Sit
Sit ua
on
Mission
Simulation
Technology
Requirements
Air Force
Organization
Congress
ACE CVN-77
Program Management
Knowledge Base 1998
Training
Objectives
Threat
Databases
N=4
DECISIONS:
Instance 8:
Balad Surface
Air Defenses
Rel Instance 4:
16
Knowledge As A
DECISION IMPACT
Theory-based
Reality
PREDICTIVE WEB
SITUATION
Semantic Web
KNOWLEDGE
=
Knowledge Theory-based
Ultimate Minimum
Decision Resource Cost
THEORY
+ INFORMATION
Ballard
Shannon
Education, Web Certification, Information Bandwidth
& Theory Capture Limit Cost
& Storage Limit Cost
-log{P(a, b, c,...x, y, z)} -log{P(a, b, c,...|....x, y, z)}
-log{P(...x, y, z)}
Theory links task specific
Theory provides performancesuccesses to most effective based measures comparing
trade-offs in training, theory Education, Theory Capture, &
creation, & technology use Knowledge Creation investment
Theory predicts that
costs can & will scale
proportionally to
Information Content
a' posteriori
a' priori
a, b, c, ...
... x, y, z
17
Axiomatic Definition of Knowledge &
Human-like Constraint-based Reasoning
Sample Application of Knowledge Measures
Axiom I. Knowledge is defined by a question or set of questions for which
the answers are initially in doubt
In General ....
0
1
7
What outputs will this black box produce? 0 0 0
2
6
3
5
4
Axiom II. Quantitative measure of knowledge requires specification of the
acceptable answer forms.
Display capable of 0 - 255
000
255
Axiom III. The number of such forms is the initial measure of Answer Uncertainty
-- i.e. Problem Size.
8
ProblemSize = 256 = 2 Possible Answers = 8 bits of Uncertainty
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.
Knowledge is measured by the amount uncertainty is reduced
EXPERIMENT
x
y
Judging knowledge content
x
y 000
0
1
7
2
6
3
5
4
0
1
2
3
4
5
6
7
0
4
16
36
64
100
144
196
THEORY
Information Content
8 = 23 answers, i.e. 3 bit Info
8 bits uncertainty
-5 bits theory
-3 bits information
0 bits answer uncertainty
Answer is always predicted
Theory 1: Answer is always Even
28 => 27 possible answers i.e. 1 bit Theory
Theory 2: Answer is multiple of 4
Theory 3: Answer y = 4 x2 or blank
Knowledge Constrains
Answer Choices
Accepted THEORIES
Constrain Us Absolutely
or Conditionally
INFORMATION Selects
Among Theoretically
Acceptable Answers
Those that Best Fit
our Current Situation
Humans willingly
consider Any choice
not viewed as
provably false,
dangerous,
or prohibitively
Expensive
Axiomatic Knowledge Measure.dsf
18
Mark 3 Memory Architecture
"Ockham's Razor" Memory Machines
" No repetition beyond necessity" -- William of Ockham,
1 4 t h C e n t ur y E ng l i s h S c h o l a s t i c P h i l o s o p h e r
Ballard's 2003 Scalability Conjecture -Non-redundant, variable length concept coding
appears to be a necessary condition for
"linear" (proportional) Storage Scalability
Conclusions -- We can simulate future computing
architectures transparently within existing
CPU machines and software operating systems.
Still we expect and plan to build "brain-like",
memory-centered declarative processing chips
within the next 5 years.
It appears likely, that the cyclic, repetitious,
processing of fixed-sized word and record
structures may be precisely the wrong design
to achieve knowledge-theoretic limit behaviors .
Ockham's Razor.dsf
19
Scaling Knowledge Management To Unlimited Size
National Strategies for Meeting Technology Challenge
RATIONAL BASELINE ANALYSIS
of Science & Technology
Source Documents
Congress
White House
National Security
S&T Council (NSTC)
Defense Appropriations
6.1
6.2
6.3
6.4
Future Year
Defense Plan
6.5
STRATEGY
TO TASK
President
Program Planning
& Budget System
Office of the
(OSD)
Chairman
Joint Chiefs of
Staff (JCS)
National
Objectives
Joint Requirements
Oversight Council (JROC)
Office of the
Secretary of the Navy
(OSN)
Asst Sec of Navy
Research, Development,
& Acquisitions
(ASN-RD&A)
Chief of Naval
Research (CNR)
Naval Aviation
Systems TEAM
(NAVAIR)
Aviation Program
Executive Officers
(PEO)
Office of Naval
Research
(ONR-03)
4.0 Research &
Engineering
Competency
Program Managers
Air (PMAs)
Naval Aviation
Science & Technology
Office (NAVSTO)
ONR 35 Database
Search Reports
Chief of Naval
Operations (CNO)
Selected Aviation
Program Element
Descriptions
Director
Navy T&E
andTechnology
Req (N091)
National Security
S&T Strategy
Defense Science &
Technology Reliance
Investment
Deputy DDR&E
Reliance Executive
Committee (EXCOM)
Fleet Command
Technology
Issues (CTI)
Basic Research
Plan (BRP)
10 panels
Budget
Categories
JWSTP & DTAP
DTO Funding
1997
Budget
Categories
TOB (C RP)
Mem orandu m
Sep t 199 4
Requirements Guidance
(STRG) June 1996
FY96 Air Materiel Command
Technology Area Plans
12 Functional Areas
2-10 Sub-Areas/Area
KRC Doc # 50
Func Sub-Area
Functional Area
Definitions
General Req
Fleet CINC
Command
Technology
Issues (CTI)
Performance Req
System/
Capability
Improved
Gen Improvem ent
Spec Impro vement
Participant
Organizations
Specific
Requirements
1995
2000
2005
2010
Nava l Aviation
S&T F unds
SBIR
Technology
Area Plans
Aviation-related
Tech Area Plans
(DTAP)
Naval Aviation
Program Sept 1995
Tech Sub-Area
Sub-Area Roadmap
Defense
Technology
Objectives
Transition Goals
Baseline
Performance
Attribute Sets
Attribute Set
Improvement
Technology Base
Sought (%)
Metrics of
Pay-off Objective Performance (MOP)
Government
Programs
Industry
Programs
Functions Needed
Naval Avi ation
S & T Program
Point of
Contact
Product Line
Phone
Product Lines
S&T Funds
5 product lines
Technology
Subdivision
Challenge
Statements
Contact
Organization
Cross Links
JW Missions
Selected Air Combat Vehicle
FY97 Thrust 1 Task Documents
Joint Mission/
Support Areas
(JMA/SA)
S&T Priority
Categories
9 categories
Focus
Areas
Priority Category
Focus Area
Required
Product/Capability
S&T Priorities
Priority Category
Fiscal Year
Performance Goal
Mid-Term Goal
Far-term Goal
Req. Product
Capability
Performance
Focus Area
Platform
Emphasis
Area
Funded Projects
Product Line
PLT Response
Current/Future
Platforms
Required
Product / Capability
Transition Ops
Needs
Goals
Challenges
Near-Term Goal
Tech Subdivision
Revolutionary
Capability
CinC/OPNAV Req
S&T Guidance
FY 94-96
Product
FY 95
Accelerated
Capabil ity
Initi ativ es ( AC I)
DTAP
Tech Subdivision
Functional
Capabilities
4-15 / objective
Element Goal
JWCA / JWCO
V isi on 2010S up po rt
Planned Activity
JWCO Roadmap
Demo Title
Concept1
CapabilityTitle
Element
Concept2
Title
Level of Support
Element Goal
Lim itation
Statement
Limitation
Elem ent Lim ita tion
P lan ne d
A c t i vi t y
Fiscal Year
Demonstration
Title
Capability Element
Concept3
Title
Demo Support
Agency Support
Element Goal
Product / Tech
P ro du ct
Key Technologies
Campaign
Objectives
S tr o n g
M o d e ra te
Level of Relation
4-48 / objective
Capability Element
Funct Capability
Level of
Relation
JWCO
Level of Support
Element
Goal
Functional Goals
Adv Tech
Demos
(ATD/ACTD)
10 op areas
JWCA Op Areas
JWCO
JWCO
Capability
Elements
Capability Element
Programmed
PE Effort
Develop. Effort
Technology
Sub-Area
Plans
Critical Issues &
Show Stoppers
Revolutionary
Mis sion
Capabil ities
Tech Subdivision
Budget $
4 concepts
2010 Op Concepts
12 objectives
Sub-Area
Development
Efforts
JWC A
Operational Areas
D e fen s e
T ech n o lo g y
O bj (D T O )
Operational
Objectives
Ad v T e ch
D e m os
(A T D )
A dv C o n ce pt
T ech D e m o s
( A C TD )
J o in t
W a r f ig h t i n g
E x p e r im e n t
(J W E )
Operational
Tasks
Te chn o log y
Defense Technology
Objectives (DTO)
WARFIGHTERS
Defense
Technology
Objective (DTO)
Key
Attribute
Requirements
Strike Warfare
KRC Doc # 8 & 9
Funds
B
A
S
E
S tr o n g
M o d e ra te
DTAP DTOs
Func Capability
Fiscal Year
10 plans
Tech Sub-Area
Baseline
Platform
Product
Capability
Deficiency
Vision 2010
Operational
Concepts
Level of Support
Area Total
Scenario
Perf ormance
Metric (MOP)
Required
Budget $
ManTech Non-Navy
Joint Warfighting
Capability
Obj ectives
(JWCO)
Level of
Support
Capability Element
Tech Area
Time Period
Assumed
Baselines
Program
Elements
KRC Doc # 36
Research, Development, & Procurement
6.1 Funds
KRC S&T KB Doc # 38
JWSTP DTOs
Defense Technology
Area Plan ( DTAP)
April 1996
Tim e Periods
10 Areas
FY 1997
Budget
Fleet
Priorities
CTI Inputs
General
Requirements
Technology
Areas
DTO Support
Number of Issues
Func Sub-Area
Assessment of gaps in DoD
Science & Technology source
documents. Led to document
redesigns & new knowledge
base initiatives. ONR 1997
6.3
Cmd Tech Issues
Sub-Area
Definitions
Implied System
Capability
6.2
DTAP &
DTO Funding
1997
Fleet
Participants
Fleet
Priority
Req Priority
Aerospace, Air Vehicles,
Avionics, C3I, & Weapons
Budget
Categories
6.3
JWCO
Support
Priority
Warfare
Priority
H - High
M - Medium
L - Low
6.1 6.2 6.3
6.2
FY 1997
Military
Objectives
Joint Warfighting
Science &Technology
Area Plan (JWS TP)
April 1996
Budget
Naval Aviation
Product Line
Teams (PLT)
R
E
S
E
A
R
C
H
Defense Science &
Technology Advisory
Group (DSTAG)
Defense
S&T Strategy
Technology Area
Panels
Oct 1994
Round Table
Transition
Opportunities
Board (TOB)
Security
Objectives
Joint Warfighting
Capability
Assessment (JWCA)
Pillars
Fleet CINC
Headquarters
S&T Round-table
Process
Joint Vision
2010
Director
Defense Research &
Engineering (DDRE)
Fiscal Year
Emphasis Area
Required
Product/Capability
Typical
Products
Requirement
Applicability
Addresses
Applies to
Related to
Potent. Product
Priority Category
Funded Project
Req Applicability
Project vs Req
Recent Tech
Transitions
Fiscal year
Platform
Engineering Phase
Transiti on Window
Engineering
Phase
6.1 6.2 6.3
TASK 1.2 VISUALLY COUPLED DISPLAY
SYS TEMS TECHNOLOGY (VCDST)
Sm art Cockpit/Crew station Proj ect (S CCP)
MAST 1 IMPROVED SITUATION AWARENESS,
TARGETING, AND MISSION MANA GEMENT
A ircrew D ecis ion A iding Interf ace (A DA I )
S mart E s cape S ys tem A nalyz er (S E SA )
A ircrew M iss ion S upport S ys tem A naly zer
(A MS SA )
Helmet S ystems:
Advanced Helmet Vision
Syste m (AHVS)
Crusader
Cyborg E ye
Joint Helmet Mount ed
Cueing System (JHMCS )
Real-Time Ret argeting Helmet
Mount ed Displays
Navy Standard Magnetic
Tracker (NSMT)
Information Fusion
3-D Out-of -cockpit Tactical Scene Rendering
2-D Mission Mapping
MAST 2 SCA LAB LE OP EN ARCHITECTURE
Open Processor Architecture
Fibre Channel Interface
Powerscene Integration
JSF 2000-2040
Avionic Architectures
Pilot Vehicle
Interfacing
Shared Int egrated RF S ensin g
Apertures
Mod ul e
Mec han ic al
& Cooli ng
COTS Additions to
Tactical Data Systems
Int egrated
Cor e
Pr ocessing
MAST 5 ADV ANCE D RF
TECHNOLOGY
UNIFIED DIGIT AL
AVIONICS NETWORK
Int egrated EO Sensing
Sh ared Aperatures
Low Band SERAT
TASK 1.3 REAL-TIME AIRBO RNE
HIGH DEFINITION IMAGE
PROCESSI NG & SELECTION
Image Processing for Helmet
or Panel Display
Vehicle
Man agement
System
WeaponSystem
Interface
High B and AS AP
Major
Accomplishments
FLAT P ANEL HELET MOUNTED DI SPLA Y
TASK 1.1 ADV ANCED COCKPIT TECHNO LO GY
Cockpit Displays:
MAST 6 ADV ANCE D
PACKA GING
Processing Algorith ms
Image Indexing & S election
Sensor Images
On/Off-board Images
Archived Images
Stores Management System
Avionics Cooling
SSA SMART SKINS
ARRAY ATD
Antenna Arrays
Structurally Embedd ed
Fabrication
Maintenance
Affordability
INDUSTRIAL BASE
HUMAN FACTORS METRICS
THRUST 1: AIR COMBAT
SITUATION AWARENESS
TASK 1.4 3-D VOLUMETRIC DIS PLAY
High Thermal Densit ies
Standard Packaging
TASK 1.6 ADVANCED
COMMON ELECTRONIC
MODULES
Battlespace Management
TASK 1.5 VEHICLE MANAGEMENT SYS TEM
MAST 4 AVIONICS INTERCONNECTS
Standard Modules
All RF S ignals
Digital Signal P roces sing
A ffo rdable/Low P ow er
MAST 3 ADVANCED PROCESSOR
Optical Network
S tat e-of- the A rt C om m er cial P ro ces sor s
Extremely High Data Rates
P arallel S ignal Processing
Commercial G raphics E ngines
Infinite Growth
V
i
s
i
o
n
s
&
A
r
c
h
i
t
e
c
t
u
r
e
s
Warfare Areas
P
l
a
t
f
o
r
m
R
o
a
d
m
a
p
s
P
r
o
d
u
c
t
R
o
a
d
m
a
p
s
Platform
Program
Elements
Strike Warfare
Deficiencies
Operations
Advisory
Groups
Mission Needs
Program
Manager
Aviation
Product
Program
Elements
Pr ogra m
Ma nager
Aviatio n
Operational
Requirements
Functional
Requirements
Performance
Requirements
Concept of
Operations
(CONOPS)
Mission
Profiles
Mission
Task
Plans
Dr. R.L. Ballard & M. Nawrocki
Strike Warfare Analysis 9/96
Knowledge Research
Huntington Beach, California, 92647
714-842-8091
Scaling to Unlimited Size.dsf
20
All Knowledge Costs Scale Proportionally
Knowledge Acquisition
by Modeling
Whole Documents
Original Document Creation Cost
$2-10 Million / original
Direct Modeling & Replace Cost
$5-7 Thousand / original
"KILLER APP" OBJECTIVE
Capture Original Content Directly
30-90% Creation Savings
Impossible
Execution or
Database Costs
Explosive
Complexity Growth
~N2
N
ar In ~
e
n
i
s
ion
re L
Opt
ts A
e
s
c
o
i
C
ho
ve C
i
s
i
Dec
5
10
15
N
Technology
Areas
Budget
Categories
6.2
Defense Technology
Area Plan (DTAP)
April 1996
Time Periods
6.3
10 Areas
DTAP &
DTO Funding
1997
FY 1997
Assumed
Baselines
KRC Doc # 36
Technology
Area Plans
Area Total
Time Period
Baseline
Sub-Area
Development
Efforts
Fiscal Year
DTO Support
Tech Sub-Area
Platform
Product
Capability
Deficiency
Scenario
Program
Elements
Tech Area
Budget
Performance
Theory
N=5
1995
2000
2005
2010
Tech Sub-Area
10 plans
Programmed
PE Effort
Develop. Effort
Technology
Sub-Area
Plans
Sub-Area Roadmap
Adv Tech
Demos
(ATD/ACTD)
Transition Goals
Baseline
Attribute Set
Improvement
Sought (%)
Pay-off Objective
Defense
Technology
Objectives
Performance
Attribute Sets
Technology Base
Metrics of
Performance (MOP)
Government
Programs
Challenge
Statements
Industry
Programs
KNOWLEDGE SUPERIORITY
Trade-offs in Minutes, not Months
N=5
Decision
Options
Competitive
Strategies
Possible
Time Period
Techno Sub-Area
Baseline Capability
"q"
Attribute Set
Improve Sought (%)
"z"
"y"
"x"
"r"
"s"
"t"
Pay-off Objective
THEORY
as an N-ary Bundle
All Knowledge Costs Proportional.dsf
21
"u"
"v"
"w"
Knowledge-based Computing
self-organizing
machines & theories
SimulatingStrike FighterAircraft's
Multi-computer Avionics Networks
F-14 A/B/D Lantrin Laser Designatior
JAST Master Plan Requirements
F/A-18 E/F & Joint Strike Fighter
F/A-18 & F-14
Strike Warfare
Testbed & Trainer
Windows NT Network of
Simulated Avionics Modules
AVIONICS PROTOTYPING TOOL
Uses one scaleable knowledge
tool to acquire, integrate,
and emulate whole network
of non-scalable databases
Simulator Test Flights Feb 1998
6 Naval & Marine Test Pilots
Patuxent River NAS
Naval Test & Evaluation Squadron 1
Pilot HOTAS
Stick, Throttle, &
Switches
Avionics
Prototyping Tool
Module Scheduler
Up Front Controller
Module
Map
Translation
Program
JFIII Real-Time
Out-of-Cockpit
Visual Display
Infra-Red Sensor
& Out-of-Cockpit
Displays
Distributed Common Object Model
(DCOM) Network
FOUNDATIONS
Mark 2
Knowledge Base
& "Virtual
Database Server"
22
IPX
Listener
Module
Maps &
Mission
Scenarios
Playback Version
Time
Sync
Talker
Module
HDD Map Module
SBIR Phase 1 1997, Phase 2 1998, Phase 3 1999
JF III Pilot-in-the-Loop
Flight Simulator Reality
Mission
Playback
Data Store
Scenario
Editor
Backseater HOTAS
Stick, Throttle, &
Switches
IPX Network
Messaging Loop
IPX Slave Target
Imaging & Scene
Rendering Modules
Copyright Knowledge Foundations 1996-2006
TEAM-MATES:
1997-1999
Knowledge Research
Avionics Prototyping Tool (APT) Virtual
Database & Flight Simulation SBIR Phase II Pacific-Sierra Research
& Mission Studio
SPONSOR: Naval Air Systems Command, Pax River
Each Avionics Systems
Manufacturer Develops
Their Own Simulation Module
Working Between Any Input
& Ouput Database Schemas
Any Input
Relational
DB Schema
Any Output
Relational
DB Schema
Any Software
Module
ASCII Outline
of Database
Model
ASCII Outline
of Database
Model
Standard Software Module Assumptions
Any Software
Module
Any Output
Relational
Any
Input
Database
Relational
Database
Any Software
Module
Any Output
Relational
Any
Input
Database
Relational
Database
Any Software
Module
Any Output
Relational
Any Input
Database
Relational
Database
Any Software
Module
Input
Data
Record
Any Output
Relational
Any Input
Database
Relational
Database
One
Knowledge
Base
Any Output
Relational
Any Input
Database
Relational
Database
One Knowledge Base Server Integrates
All Schema & Emulates Exactly Each
Module's Expected Database Behaviors
Input
Data
Record
Output
Data
Record
AVIONICS
MODULE
AVIONICS
MODULE
Any Output
Relational
Any Input
Database
Relational
Database
Input
Data
Record
APT Database Integration.dsf
Input
Data
Record
Any Software
Module
Output
Data
Record
Output
Data
Record
AVIONICS
MODULE
AVIONICS
MODULE
Input
Data
Record
Any Output
Relational
Any
Input
Database
Relational
Database
Input
Data
Record
Output
Data
Record
Copyright Knowledge Research 1997-2006
23
Input
Data
Record
Output
Data
Record
AVIONICS
MODULE
Input
Data
Record
SIMULATION
SCHEDULER
Any Software
Module
Output
Data
Record
AVIONICS
MODULE
AVIONICS
MODULE
Any Software
Module
Output
Data
Record
AVIONICS
MODULE
Input
Data
Record
Output
Data
Record
Output
Data
Record
AVIONICS
MODULE
NEED TO MATCH CONCEPTS ACROSS
ALL KNOWLEDGE SOURCES
KNOWLEDGE RESEARCH
AGGREGATE AEROSPACE
KNOWLEDGE ACQUISITION
PROJECTS 1991-1996
Delivered June 1991
Budget planning and
warfare assessment
$350K
OPNAV FastPlan
Summary Warfare
Appraisal
Knowledge Base
$30K
NASA Space
Explorations
Technology
Concept Demo
Delivered June 1992
Technology &
Economic Impact
Delivered January 1993
Cataloging Current
$150K
Facilities and
Capabilities
Delivered January 1994
Cataloging and
integrating simulators,
models, data sources
NAWC
Joint Service
T&E Capabilities
Knowledge Base
Joint Service
Universal Threat
$350K Simulation System
Knowledge/Model Base
SBIR Phase 1A
Delivered May 1994
Building TPIPT
Roadmaps
$85K
AFSOC
Technology
Roadmap
Knowledge Base
Product Prototype
Delivered Dec 1994
$85K
Carroll's Government
& Defense
Organization
Knowledge Base
Technology Capture
& Integration
Initial Designs
to AIR-531
Phase I Warfighting &
Systems Technology
Requirements
Demonstrated &
Delivered May 1995
$200K
$250K
Aircrew Systems for
Precision Strike
Technology Integration
Knowledge Base
Joint Advanced
Strike Technology
(JAST)
Knowledge Base
$1.5 Million labor + $350K licenses
International
Organizations
Allied
Governments
Military
Facilities
Intelligence
Organizations
Government
Organization
Threat
Geography
Threat
Scenarios
Operational
Forces
Navy
Organization
DoD
Organization
Threat
Databases
Training
Objectives
Mission
Task Analysis
Training
Requirements
CrewSTANDARDS
Centered
Warfare INDUSTRY
Requirements
Requirements
Integrating Knowledge
Training
DesignMultiple Sources
Across
Facilities
Model
Base
Provided By Different
Developers
Mission
Technology
Operational
Concepts
Mission
Requirements
Simulation
Requirements
USER'S WORKING
LEVELS
Air Force
Organization
R&D
User Work-In-Progress Algorithmic
Layers
Technology
Programs
Model
Testbeds
User Work Products & OverlaysBase
Layer
Technology
Procurment
System
Congressional
WORKGROUP
SHARED ASSETS
Integration
Programs
Simulation
Committees
Model BaseRequirement
& Assumption Overlays
System
Political &
Performance
Major Defense
Major Weapon
Performance
Economic
Analysis
Contractors
Systems
Requirements
Base
ModelASSETS
Base
Cost &
CORPORATE KNOWLEDGE
System
Technology
Major
Manufacturing
TestOverlays
Risk
SubsystemsLatest Information
Associations
Facilities
Modeling
Integration
System Overlays
Major
Manufacturing
Test
Aftermarket
Overlays
Subcontractors
Technology
Copyright
MARK 2 BUILDER
Requirements
Congress
Patterns of Thought
KNOWLEDGE RESEARCH
1990-2005
1990-2001
Theory-based
Semantics
Industry-wide Asset Integration.dsf
24
"A noun is a sound that has meaning
only by convention,
convention,
there is no natural relationship
between any idea or observation and
the sound that you utter to describe it."
Aristotle -- On Interpretation,
Interpretation, 350 B.C
Invented Logic as a Convention for Grammar Rules
CONCLUSION:
No match-up of words to meaning
can ever be -- provably right or wrong.
Our commitment to assigning meaning
to any idea must rest upon establishing
its necessity and clear importance
as a "natural
" natural relationship."
Aristotle Quote.dsf
25
"The ontology of a theory consists in the
objects theory assumes there to be."
Quine -- Word and Object, 1960
Theories are to be accepted or rejected
as a whole.
whole.
If we choose to accept and use a theory
for reasoning, then we must likewise
commit to all the ideas and relationships
the theory needs to establish its existence.
The Orderly Evolution of scientific knowledge requires that
theoretical projections remain explicitly linked through
those Ideas and Relationships that their theories have assumed
If and when theories are rejected, their links dissolve
dissolve..
Quinw Quote.dsf
26
ON THE RACE TO A FIXED
KNOWLEDGE STANDARD
A foolish consistency is the hobgoblin of little minds,
.....
Speak what you think now in hard words, and
to-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
Foolish Consistencies.dsf
27
28
Semantic Value Spectrum:
Different capabilities power different levels of return
29
Semantic Bandwidth:
Value gains from two-fold to more than 100 times
30
Semantic Wave Market Growth
31

Knowledge Foundations

Transcription

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