Download (I) · Cognitive Informatics

University of Vienna / Universitat Wien
Latest Development in
Cognitive Informatics (CI)
Yingxu Wang, Prof., PhD, PEng, FWIF, SMIEEE, SMACM
President, International Institute of
Cognitive Informatics and Cognitive Computing (IICICC)
Dept. of Electrical and Computer Engineering
University of Calgary, Canada
Email: [email protected]
http://www.enel.ucalgary.ca/TESERC
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 1
IICI*CC
1. Introduction
► 1. Introduction
2. Historic Development of Cognitive Informatics (CI)
3. The Latest Advances in CI
- The theoretical framework of CI
- The formal models of the brain (LRMB)
- Abstract intelligence (αI)
- Neural informatics (NeI) and the OAR mode of memory
- The nature of knowledge and learning
- Denotational mathematics (DM) for CI
4. Applications of CI
5. Conclusions
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 2
IICI*CC
CI and NI – The Last World Yet to be Explored
• The Nature of Intelligence
- In the narrow sense, is a
human or a system ability
that transforms information
into behaviors;
- In the broad sense, is any
human or system ability that
autonomously transfers the
forms of abstract information
between data, information,
knowledge, and behaviors in
the brain.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 3
IICI*CC
History of NI Studies
• 2500BC: The ancient Egyptians believe that the heart is the
true seat of intelligence.
• 450BC: Greek physician, Alcmaeon, concluded that the
brain is the central organ of sensation and thought on the
basis of anatomic dissection of animals.
• 350 BC: Plato, Greek philosopher, observed that
philosophy begins in human wonder, a powerful
desire to understand the world, not merely to act
in it as animals do.
• 335BC: Aristotle states that the organ of thought
and sensation is the heart, and the brain is a
radiator to cool it.
• 300BC: Herophilus and Erasistratus first dissect
a human body, and find nervous system of the brain.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 4
IICI*CC
History of NI Studies (Cont’d)
• 1649: Rene Descartes proposes that the brain
functions like a machine.
• 1664: Thomas Willis writes the first monograph on brain
anatomy and physiology. A set of terms, such as neurology,
hemisphere, and lobe, is introduced.
• 1872: Charles Darwin studies the expression of
emotions in man and animals, and finds humans
blush indicates self-consciousness.
• 1875: Wilhelm Wundt sets up the first lab devoted to
study human behavior – the Institute for
Experimental Psychology.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 5
IICI*CC
History of NI Studies (Cont’d)
• 1900: Sigmund Freud describes the unconsious
mind drives much of human behavior in his book:
The Interpretation of Dreams.
• 1906: S. R. y Cajal and C. Golgi win a Nobel Prize on the
structure and function of nerve cells.
• 1932: L. E. Adrian and C. Sherrington win a Nobel Prize on
neuron function for transmitting nerves messages.
• 1967: H.R. Granit, H.K. Hartline, and G. Wald win a Nobel
Prize on physiological and chemical visual processes in the
eyes.
• 1981: T. Wiesel, D. Hubel and R. Sperry win a Nobel Prize
on how visual information is transmitted from the retina to
the brain.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 6
IICI*CC
The IME-I Model of the General Worldview
The abstract world (AW)
I
The natural world
(NW)
I
M
E
The physical world (PW)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 7
IICI*CC
Cognitive Informatics (CI)
• Cognitive informatics (CI) is a transdisciplinary enquiry
of computer science, information science, cognitive
science, and intelligence science, which studies:
- The internal information processing mechanisms and
processes of natural intelligence;
- The theoretical framework and denotational
mathematics of abstract intelligence; and
- Their engineering applications by cognitive computing.
Cognition
Science
Neurobiology
Psychology
Modern
Information
Science
Computing
Science
Mathematics
Cognitive Informatics (CI)
Relationship between CI and Traditional Informatics
Cognitive
Informatics
(Internal,
Brain-based)
Modern
Informatics
(External,
Classical
Informatics
(External,
Computer-based)
Channel-based)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 9
IICI*CC
Related Books (I)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 10
IICI*CC
Related Books (II)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 11
IICI*CC
Related Books (III)
[16] Wang, Y. (2011), Cognitive Informatics: Foundations
of Natural, Abstract, and Computational Intelligence,
MIT Press, to appear.
[15] Wang, Y. (2011), Denotational Mathematics:
Rigorous Means for Software Science and Cognitive
Informatics, MIT Press, to appear.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 12
IICI*CC
Invited Keynote Speeches (I)
[21] Wang, Y. (2010), Cognitive Computing and World Wide Wisdom
(WWW+), Proc. 9th IEEE Int’l Conf. Cognitive Informatics (ICCI'10),
Tsinghua Univ., Beijing, July 8.
[20] Wang, Y. (2010), Cognitive Informatics and Denotational
Mathematics Means for Brain Informatics, 1st Int’l Conference on Brain
Informatics (ICBI'10), Toronto, Aug. 29.
[19] Wang, Y. (2009), Cognitive Computing and Machinable Thought, 8th
IEEE Int’l Conference on Cognitive Informatics (ICCI'09), Hong Kong,
June 17.
[18] Wang, Y. (2009), On the Origin and Embodiment of Consciousness
in Cognitive Informatics and Computational Intelligence, Int’l
Conference Toward a Science of Consciousness, Hong Kong, June 14.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 13
IICI*CC
Invited Keynote Speeches (II)
[17] Wang, Y. (2009), Theoretical and Empirical Foundations of Software
Engineering, Institute of Software, Chinese Academy of Sciences,
Beijing, June 25.
[16] Wang, Y. (2008), On Abstract Intelligence and Its Denotational
Mathematics Foundations, 7th IEEE Int’l Conference on Cognitive
Informatics (ICCI'08), Stanford University, USA, August 15.
[15] Wang, Y. (2008), On Cognitive Computing and Denotational
Mathematics, IEEE 2008 Int’l Workshop on Semantic Computing and
Systems (WSCS’08), Huang-Shan, China, July 21.
[14] Wang, Y. (2008), Cognitive Informatics and Cognitive Computing,
University of California, Berkeley, Oct. 14.
[13] Wang, Y. (2008), On Theoretical Foundations of Software
Engineering and Denotational Mathematics, 5th Asian Workshop on
Foundations of Software, July 18.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 14
IICI*CC
Invited Keynote Speeches (III)
[12] Wang, Y. (2007), Cognitive Informatics Foundations of Nature and
Machine Intelligence, 6th Int’l Conference on Cognitive Informatics
(ICCI’07), Lake Tahoe, CA, Aug. 7.
[11] Wang, Y. (2006), Cognitive Informatics - Towards the Future
Generation Computers that Think and Feel, 5th IEEE Int’l Conference on
Cognitive Informatics (ICCI'06), Beijing, China, July 18.
[10] Wang, Y. (2005), Psychological Experiments on the Cognitive
Complexities of Fundamental Control Structures of Software Systems,
4th IEEE Int’l Conference on Cognitive Informatics (ICCI'05), UC Irvine,
USA, August 9.
[9]Wang, Y. (2006), Cognitive Informatics - Towards the Future
Generation Computers that Think and Feel, 5th IEEE Int’l Conference on
Cognitive Informatics (ICCI'06), Beijing, July 18.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 15
IICI*CC
Invited Keynote Speeches (IV)
[8] Wang, Y. (2004), On Autonomic Computing and Cognitive Processes,
3rd IEEE Int’l Conference on Cognitive Informatics (ICCI'04), Victoria,
BC, Canada, August.
[7] Wang, Y. (2003), On Cognitive Mechanisms of the Eyes: the Sensor
vs. the Browser of the Brain, 2nd IEEE Int’l Conference on Cognitive
Informatics (ICCI'03), London, UK, August.
[6] Wang, Y. (2003), Cognitive Informatics Models of Software Agent
Systems and Autonomous Computing, Int’l Conference on Agent-Based
Technologies and Systems (ATS'03), Calgary, Canada, August.
[5]Wang, Y. (2002), The Latest Development on Cognitive Informatics,
8th Int’l Conference on Object-Oriented Information Systems (OOIS'02),
Montpellier, France, Sept.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 16
IICI*CC
Invited Keynote Speeches (V)
[4] Wang, Y. (2002), On Cognitive Informatics, First IEEE International
Conference on Cognitive Informatics (ICCI'02), Calgary, Canada, August.
[3] Wang, Y. (2002), On the Information Laws of Software, First IEEE
International Conference on Cognitive Informatics (ICCI'02), IEEE CS
Press, Calgary, AB., Canada, August.
[2] Wang, Y. (2002), A New Mathematics for Software Engineering: The
Real-Time Process Algebra (RTPA), 2nd ASERC Workshop on
Quantitative and Soft Computing Based Software Engineering
(QSSE'02), Banff, AB, Canada, February.
[1] Wang, Y. (2000), Progresses and Trends in Software Engineering,
The 2000 Conference of IEEE Sweden, Stockholm, May.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 17
IICI*CC
International Collaborations at Stanford, UC Berkeley and Oxford
International Collaborators
• Prof. Bernard Widrow, father of neural nets, Stanford Univ.
• Prof. Lotfi Zadeh, father of fuzzy Logic, UC Berkeley
• Prof. Tony Hoare, Computer science and SE, Oxford Univ.
• Prof. Jean-Claude Latomb, Robotics and AI, Stanford Univ.
• Prof. Witold Pedrycz. Computational intelligence, Univ. of
Alberta, Canda
• Prof. Witold Kinsner, Space signal cognition, Univ. of
Manitoba, Canada
• Prof. Jim Anderson, Cognitive Science, Brawn Univ.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 19
IICI*CC
2. Historical Development of CI
1. Introduction
► 2. Historic Development of Cognitive Informatics (CI)
3. The Latest Advances in CI
- The theoretical framework of CI
- The formal models of the brain (LRMB)
- Abstract intelligence (αI)
- Neural informatics (NeI) and the OAR mode of memory
- The nature of knowledge and learning
- Denotational mathematics (DM) for CI
4. Applications of CI
5. Conclusions
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 20
IICI*CC
IEEE ICCI 2002 (UofC, Calgary, Canada)
ICCI 2002, Calgary
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 21
IICI*CC
IEEE ICCI 2003 (London SBU, UK)
ICCI 2003, London
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 22
IICI*CC
IEEE ICCI 2004 (U Vic., Victoria, Canada)
ICCI 2004, Victoria
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 23
IICI*CC
IEEE ICCI 2005 (UC Irvine, USA)
ICCI 2005, Irvine, CA
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 24
IICI*CC
IEEE ICCI 2006 (CAS, Beijing, China)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 25
IICI*CC
IEEE ICCI 2007 (Lake Tahoe, USA)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 26
IICI*CC
IEEE ICCI 2008 (Stanford Univ., USA)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 27
IICI*CC
IEEE ICCI 2009 (HKPolyU, Hong Kong)
ICCI 2009
IEEE
ICCI
ICfCI
IJCiNi
The 8th IEEE International Conference on
Cognitive Informatics (ICCI’09)
June 15-17, 2009, Hong Kong
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 28
IICI*CC
IEEE ICCI 2010 (Tsinghua Univ., China)
The 9th IEEE International Conference on
Cognitive Informatics (ICCI’10)
July 7-9, 2010, Tsinghua University, Beijing
IEEE ICCI 2011 (Banff, Canada)
ICCI*CC 2011
IEEE
ICCI
ICfCI
IJCiNi
The 10th IEEE International Conference on
Cognitive Informatics & Cognitive Computing
(ICCI*CC’11)
http://www.enel.ucalgary.ca/ICCICC11/
Aug. 8-10, 2011, Banff, Canada
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 30
IICI*CC
Related Journals - 1 : IJCINI
Editor-in-Chief Prof. Yingxu Wang
ISSN: 1557-3958
http://www.enel.ucalgary.ca/IJCINI/
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 31
IICI*CC
Related Journals - 2: IJSSCI
International Journal of Software Science
and Computational Intelligence (IJSSCI)
Editor-in-Chief: Prof. Yingxu Wang
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 32
IICI*CC
ASP Journal of Advanced Mathematics and
Applications
3. The Latest Advances in CI
1. Introduction
2. Historic Development of Cognitive Informatics (CI)
► 3. The Latest Advances in CI
- The theoretical framework of CI
- The formal models of the brain (LRMB)
- Abstract intelligence (αI)
- Neural informatics (NeI) and the OAR mode of memory
- The nature of knowledge and learning
- Denotational mathematics (DM) for CI
4. Applications of CI
5. Conclusions
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 34
IICI*CC
3.1 The Theoretical Framework of CI
The Theoretical Framework of Cognitive Informatics (CI)
CI
Theories (T)
CI
Applications (A)
Descriptive
Mathematics for
CI (M)
T1
The IME
model
T2
The LRMB
model
T7
CI laws of
software
M1
Concept
algebra (CA)
A1
Future generation
Computers
T3
The OAR
model
T8
Perception
processes
M2
RTPA
A2
Capacity of human
memory
T4
CI model of
the brain
T9
Inference
processes
M3
System algebra
(SA)
A3
Autonomic
computing
T5
Natural
intelligence
T10
The knowledge
system
T6
Neural
informatics
A9
Cognitive complexity
of software
A4
Cognitive properties
of knowledge
A5
Simulation of
cognitive behaviors
A8
Deductive semantics
of software
A7
CI foundations of
software engineering
A6
Agent
systems
3.2 The Formal Model of the Brain
L7 – Higher
cognition
L
R
M
B
Learning
Problem
Solving
L6 –
Meta-inference
Deduction
Analysis
L5 –
Meta-cognition
IdentifyObj
Abstraction
L4 –
Perception
Attention
L2 –
Memory
SBM
L1 –
Sensation
Vision
Emotions
STM
Audition
Decission making
Synthesis
…
Search
…
LTM
Smell
…
Memorize
L3 –
Action
…
…
ABM
CPM
Tactility
Taste
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 36
IICI*CC
Conventional Technologies for Exploring the Brain
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 37
IICI*CC
Imaging the Function of a CPU by its Layout?
• Without understanding the logical and functional models and mechanisms of
a CPU, nobody can explain the functions of it by fine pictures of the intricate
interconnections of millions of transistors (gates).
• It would be more confusing because the control unit (CU) and ALU of the CPU
and its buses are always active for almost all different kind of operations. So
do, unfortunately, brain science and neurobiology.
The Layered Reference Model of the Brain (LRMB)
LRMB: Configuration of Processes
Life behaviors and complex actions
Layer 7: The higher cognitive processes
Comprehension
Learning
Problem
solving
Decision
making
Creation
Planning
Pattern
recognition
Layer 6: Meta inference processes
Deduction
Induction
Abduction
Analogy
Analysis
Synthesis
Layer 5: Meta cognitive processes
Object
Identify
Abstra- Concept
ction establish.
Categori- Compa- Memori- Qualifi- Quantifi- Selection Search
zation
rison
zation
cation
cation
Model
Imagery
establish.
Layer 4: Action processes
Wired actions
(Skills)
Contingent actions
(Temporary behaviors)
Layer 3: Perception processes
SelfConsciousness
Attention
Motivation and
goal-setting
Emotions
Attitudes
Sense of
spatiality
Sense of
motion
Layer 2: Memory processes
Sensory buffer
Memory
Short-term
Memory
Long-term
Memory
Action buffer
Memory
Layer 1: Sensational processes
Vision
Audition
Smell
The physiological/neurological
Brain
Tactility
Taste
3.3 Abstract Intelligence (αI)
• Abstract intelligence, αI, is a human enquiry of both
natural and artificial intelligence at the embody levels
of neural, cognitive, functional, and logical from the
bottom up.
No.
Paradigms of intelligence
Embodying Means
1
Natural intelligence (NI)
Naturally grown biological and
physiological organisms
2
Artificial intelligence (AI)
Cognitively-inspired artificial models
and man-made systems
3
Machinable intelligence (MI)
Complex machine and wired systems
4
Computational intelligence
(CoI)
Computational methodologies and
software systems
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 41
IICI*CC
A Profound Wonder on Natural Intelligence
• Intelligence is a driving force or an ability to acquire and
use knowledge and skills, or to inference in problem
solving.
• How conscious intelligence is generated as a highly
complex cognitive state in human mind on the basis of
biological and physiological structures?
• How natural intelligence functions logically and
physiologically?
• One of the key objectives in cognitive informatics is to
seek a coherent theory for explaining the nature and
mechanisms of both natural and artificial intelligence.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 42
IICI*CC
The Generic Abstract Intelligence Model
(GAIM)
K
LTM
Stimuli
Ir
D
B
Ic
SBM
Enquiries
I
Ip
Behaviors
ABM
Ii
STM
Ip – Perceptive intelligence
Ii – Instructive intelligence
Ic – Cognitive intelligence
Ir – Reflective intelligence
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 43
IICI*CC
The GAIM of αI
• The nature of abstract intelligence states that αI can be
classified into four forms called the perceptive
intelligence Ip, cognitive intelligence Ic, instructive
intelligence Ii, and reflective intelligence Ir as modeled
below:
α I  Ip : D → I (Perceptive)
|| Ic : I → K (Cognitive)
|| Ii : I → B (Instructive)
|| Ir : D → B (Reflective)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 44
IICI*CC
Theoretical Framework of αI
Logical model
Dimension of
paradigms
Functional model
Computational
Intelligence
Machinable
Intelligence
Abstract
Intelligence
(αI)
Dimension of
embodying
means
Artificial
Intelligence
Natural
Intelligence
Cognitive model
Neural model
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 45
IICI*CC
Compatibility of Paradigms of Intelligence
• Compatible intelligence states that natural intelligence
(NI), artificial intelligence (AI), machinable intelligence
(MI), and computational intelligence (CoI), are
compatible by sharing the same mechanisms of αI, i.e.:
CoI  MI  AI  NI  I
CoI  MI  AI  NI  I
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 46
IICI*CC
3.4 Neural Informatics (NeI) and the OAR model of Memory
• Neural Informatics (NeI) is a new interdisciplinary enquiry of the
biological and physiological representation of information and
knowledge in the brain at the neuron level and their abstract
mathematical models.
• The cognitive models of memory (CMM) states that the architecture
of human memory is parallel configured by the Sensory Buffer
Memory (SBM), Short-Term Memory (STM), Long-Term Memory
(LTM), Consciousness Status Memory (CSM), and Action-Buffer
Memory (ABM), i.e.:
CMM  SBM
|| STM
|| CSM
|| LTM
|| ABM
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 47
IICI*CC
The Representation of Long-Term Memory
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 48
IICI*CC
The OAR Model of Memory Architecture
OAR = (O, A, R)
O – object
A – attribute
R – relation
LTM: A hierarchical and partially connected neural clusters.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 49
IICI*CC
The Mathematical Model of OAR
MC = C
In the Memory Capacity (MC)
model:
m
n
n!
=
m !(n − m)!
n - the total number of
neurons
11
10 !
= 3
11
3
10 !(10 − 10 )!
m - the number of average
partial connections
between neurons.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 50
IICI*CC
The Finding of Human Memory Capacity
n
C
m
n
n!
=
m !( n − m)!
∏
i
i =n − m +1
m!
n
ln(
=
∏ i)
Consider=
ln( n !)
i =1
ln Cnm
=
n
∑
n
∑ ln i
i =1
m
i =n − m +1
ln i − ∑ ln i
i =1
Given m = 103 , and n = 1011 :
n
10
C10
=
11
3
m
∑ ln i − ∑ ln i )
i =n − m +1
i =1
e=
(
108,432
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 51
IICI*CC
Advances of the Human Brain and NI
• The quantitative advantage of human brain states that
the magnitude of the memory capacity of the brain is
tremendously larger than that of the closest species.
• The qualitative advantage of human brain states that
the possession of the abstract layer of memory and the
abstract reasoning capacity makes human brain
profoundly powerful on the basis of the quantitative
advantage.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 52
IICI*CC
3.5 The Nature of Knowledge and Learning
The Cognitive Information Model (CIM)
Type of output
Information
Type of input
Ways of
acquisition
Action
Information
Knowledge
(K)
Intelligence Direct or
indirect
(I)
Action
Experience
(E)
Skill
(S)
Direct only
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 53
IICI*CC
Knowledge and Learning
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 54
IICI*CC
Knowledge Representation in Concept Algebra
c3
pen
c1
stationery
O1
fountain
ballpoint
o11
o12
printer
Knowledge
level (K)
c2
O2
o13
o21
brush
o22
laser
Object level
(U)
Ink-jet
A2
A1
a1
a2
a3
a writing using having
tool
ink
a nib
a4
A5
A6
with an ink a printing using
container
tool
papers
…
A7
with a toner
cartridge
Attribute level
(M)
The Mathematical Model of Knowledge in the Brain
• The abstract object knowledge K in the brain is a
perceptive representation of information by a function rk
that maps a given concept C0 into all related concepts,
i.e.:
K  rk : C0 → (
n
XC ), r ∈ R
i
k
i =1
• The entire knowledge K is represented by a concept
network, which is a hierarchical network of concepts
interlinked by the set of nine associations ℜ defined in
concept algebra, i.e.:
n
n
i=1
j=1
K = ℜ : XCi → XC j
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 56
IICI*CC
Dynamic Knowledge Representation in OAR
• The principle of dynamic knowledge representation
states that internal memory in the form of an OAR
structure can be updated by a conjunction between the
existing OAR and the newly created sub-OAR (OARnew),
i.e.:
+
OAR’ ST ≙ OARST  OARnewST
= OARST + (Onew, Anew, Rnew)

Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 57
IICI*CC
The Cognitive Model of Knowledge
The external world
The internal world
The Image Layer
Real Entities
RE1
RE2
…
REn
Virtual Entities
Objects
The external world
The Abstract Layer
The Image Layer
Attributes
Virtual Entities
Relations
Real Entities
VE1
O1
A11
R11
VE1
RE1
VE2
O2
A12
R12
VE2
RE2
…
VEn
Meta
objects
Derived
objects
…
…
…
On
A1x
R1p
O’1
Am1
Rm1
O’2
Am2
Rm2
…
O’m
…
Amy
…
Rmq
…
VEn
…
REn
The Cognitive Process of Memorization
Memorization (I:: c(OS, AS, RS)ST; O:: OAR’ST) =
{I. Encoding
c(OS, AS, RS)ST → sOARST
// Concept representation
II. Retention
→ OAR’ST := OARST  sOARST
// Update OARST in LTM
III. Rehearsal
→  RehearsalBL = T
(IV. Retrieval
 Search (I:: OARST; O:: sOARST |
(OS, AS, RS)ST ⊆ OARST)) // Retrieval sOARST in LTM
V. Decoding
→ (sOARST → c’(OS, AS, RS)ST)
)
// Concept reconstruction
VI. Repeat
→  (c’(OS, AS, RS)ST) ~ c(OS, AS, RS)ST)
→⊗
// Memorization succeed
|~
 Memorization (I:: c(OS, AS, RS)ST; O:: OAR’ST)
}
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 59
IICI*CC
The Cognitive Process of Learning
Learning (I:: OS; O:: OAR’ST) =
{I. Identify object
 ObjectIdentification (I:: OS; O:: AS)
// AST – a set of attributes of OS
II. Concept establishment
 ConceptEstablishment (I:: OS, AS; O:: c(OS, AS, RS)ST)
III. Comprehension
 Comprehension (I:: c(OS, AS, RS)ST; O:: sOAR’ST)
IV. Memorization
 Memorization (I:: sOARST; O:: OAR’ST)
V. Rehearsal
→  Rehearsal BL = T
( ( ConceptEstablishment (I:: sOARST; O:: c(OS, AS, RS)ST)
|| → Comprehension (I:: sOARST; O:: sOARST)
)
 Memorization (I:: sOARST; O:: OAR’ST)
)
|~
→⊗
}
3.6 Denotational Mathematics for CI
• Mathematics
- The abstract science of numbers, quantity, and space, as well as
their relations.
- The meta-methodology of sciences and engineering
• Essences of Mathematics
- Abstraction
- Quantification
- Elicit/identify generic abstract entities
- Manipulate symbols
- Establish axiomatic rules
- Provide rules of derivations
• Basic Methodology of Mathematics
- Abstraction
- Symbolic inferences
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 61
IICI*CC
What is DM
• Denotational mathematics (DM) is a category of
expressive mathematical structures that deals with
high-level mathematical entities beyond numbers and
sets, such as abstract objects, complex relations,
perceptual information, abstract concepts, knowledge,
intelligent behaviors, behavioral processes, and
systems.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 62
IICI*CC
Why Mathematics is Important to CI?
• The Irreplaceable Role of Applied Mathematics
- The objects under study are essentially a mathematical one
- Generic, abstract, persistent, complex, awaiting for qualification
- Any persistent and nontrivial problem in a discipline is a
mathematical challenge for abstraction (generalization/modeling)
and quantification, and formal inferences.
• Example – Mathematical Foundations of Computing
- Boolean (1854): The Laws of Thought
- Russel (1900): The Principles of Mathematics
- Turing (1950): Computing Machinery and Intelligence
- von Neuwmann (1966): Automata and SPC
• Example – AI and CI
- Zadeh (1965/2008): Fuzzy logic
- Chomesky (1956): Formal linguistics
- Donotational mathematics …
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 63
IICI*CC
New Problems Need New Forms of Mathematics
• Although there are various ways to express human and system
actions and behaviors, it is found in cognitive informatics that agent
behaviors may be classified into three basic categories:
- to be: mathematical logic
- to have: set theory
- to do: process algebra
• All mathematical means and forms, in general, are an abstract
description of these three categories of system behaviors and their
common rules.
• All existing mathematics, continuous or discrete, are mainly analytic,
seeking unknown variables from known factors according to certain
functions.
• We need a new mathematics, i.e. Denotational Mathematics (DM), that
can describe a solution of a software application simply, formally,
and expressively.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 64
IICI*CC
DM for Knowledge and Intelligence Processing
Function
Category
Mathematical Means
Conventional
Identify objects &
attributes
To be
Describe relations &
possession
Describe status and
behaviors
(|=)
Denotational
Logic
Concept algebra
To have (|⊂)
Set theory
System algebra
To do
Functions
Real-time
process algebra
(RTPA)
(|>)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 65
IICI*CC
Theoretical Framework of DM
The Theoretical Framework of Denotational Mathematics (DM)
New mathematical
entities
Information
New expressive
needs in DM
Novel mathematical
forms of DM
Application areas
of DM
System
architectures
Process
algebra
Cognitive
informatics
System
behaviors
Concept
algebra
Computational
intelligence
Processes
Series of
actions
System
algebra
Software
engineering
Behaviors
Series of
decisions
Fuzzy/rough
set theories
Knowledge
engineering
Knowledge
representation
Category
theory
Information
engineering
Concepts
Knowledge
Systems
Complex
relations
Pattern
description
Distributed
granules
Autonomic computing
Cognitive computers
Neural informatics
Paradigms of DM
No.
1
Paradigm
Structure
Concept algebra CA  (C , OP, Θ) = ({O, A, R c , Ri , R o },
{•r , •c }, ΘC )
Mathematical entities
c  (O, A, R c , Ri , R o )
2
System algebra
SA  ( S , OP, Θ) = ({C , R c , Ri , R o , B, Ω }, S  (C , R c , R i , R o , B, Ω, Θ)
{•r , •c }, Θ)
3
Real-time
process algebra
RTPA  (T, P, N)
(RTPA)
Algebraic operations
Algebraic
manipulations on
  
c  {,     } abstract concepts
r  {     }
Algebraic
manipulations on
  
c  {, ,     } abstract systems
r  {  }
P {:=, , ⇒, ⇐, , , , |, R  {→, , |, |…|…,
|, @, , ↑, ↓, !, , , §}
T  {N, Z, R, S, BL, B, H, P, TI, D, DT,
Usage
Algebraic
manipulations on
, , ||, ∯, |||, »,  ,  t,  e,  i} abstract processes
,
,
,
RT, ST, @e S, @t TM, @int ,
s BL}
4
Visual semantic VSA  (O, VSA )
algebra (VSA)
= ({H  S  F  L}, VSA )
H  {, , , , , , ,
, , , }
S  {C u , Rs ,C y , S p ,Co , Py }
F  {, §, }
L  {, , , , , }
VSA  { , , , , , , , ,
@(p),@(x,y,x),  }
Algebraic
manipulations on
abstract visual
semantics
Applications of DM in αI and CI
No.
Application
1 Iterative and recursive behaviors
2 The generic math model of programs
3 Cognitive processes of the brain in LRMB
4 The consciousness process
5 The memorization process
6 Formal inference processes
7 Internal knowledge representation
8 Autonomous machine learning
9 Intelligent search engines
10 Systems modeling
11 Granular computing
12 Long-life-span systems
13 Visual object identification
14 Pattern recognition
15 Cognitive computers
Form of DM Reference
RTPA
[30]
[17]
[35]
[36]
[31]
[22]
Concept
[19]
algebra
[7, 21]
[4]
System
[25]
algebra
[37]
[32]
VSA
[29]
[28]
Combined DM [16, 38]
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 68
IICI*CC
4. Applications of CI
1. Introduction
2. Historic Development of Cognitive Informatics (CI)
3. The Latest Advances in CI
- The theoretical framework of CI
- The formal models of the brain (LRMB)
- Abstract intelligence (αI)
- Neural informatics (NeI) and the OAR mode of memory
- The nature of knowledge and learning
- Denotational mathematics (DM) for CI
► 4. Applications of CI
5. Conclusions
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 69
IICI*CC
Computing Power: Speed vs. Intelligence
αI
Normal
human
intelligence
Computer
speed
3 year
old kits’
intelligence
AI
//
1940s
1950s
t
1980s
2010s
Computational intelligence is not merely a speed issue!
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 70
IICI*CC
Applications Areas of CI
• A wide range of applications of CI have been identified
such as:
- The infrastructures of collective intelligence
- Networks for computational intelligence providing
- Distributed agent networks
- Distributed cognitive robots
- Distributed cognitive sensor networks
- Distributed remote control systems
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 71
IICI*CC
Cognitive Computers (cCs)
• Cognitive Computers
A cognitive computer (cC) is a category of intelligent
computers that think, perceive, learn, and reason.
• cCs are designed for knowledge processing as that of
a conventional von Neumann computer for data
processing.
• cCs are able to embody machinable intelligence
such as computational inferences, causal analyses,
knowledge manipulation, learning, and problem solving.
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 72
IICI*CC
CC vs. von Neumann Computers
Facet
Conventional
data computing (DC)
Cognitive computing
(CC)
Objects
Abstract bits
Structured data
Concepts (Words)
Syntax
Semantics
Basic
operations
Logic
Arithmetic
Functional
Concept identification
Syntactic analyses
Semantic analyses
Advanced
operations
Algorithms
Processes
Programs
Concept formulation
Knowledge representation
Comprehension
The
Learning
Cognitive
Inferences
CPU
Causal analyses
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 73
IICI*CC
Cognitive Robots
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 74
IICI*CC
Cognitive Robots using Visual Semantic Algebra
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 75
IICI*CC
The Cognitive Learning Engine (CLE)
• Learn common or professional knowledge faster than
human does
• Learn and process knowledge continually beyond the
natural memory creation constraints of humans
• They may never forget a piece of learned knowledge
once that has been cognized and memorized
• Most excitingly, they can directly transfer learned
knowledge to peers without requiring re-learning
because they use the same knowledge representation
model and manipulation mechanisms
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 76
IICI*CC
Cognitive Computing Based on Concept Algebra
(1/3)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 77
IICI*CC
Cognitive Computing Based on Concept Algebra
(2/3)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 78
IICI*CC
Cognitive Computing Based on Concept Algebra
(3/3)
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 79
IICI*CC
eBrain: Simulations of the LRMB (3/3)
L7 – Higher
cognition
L
R
M
B
Learning
Problem
Solving
L6 –
Meta-inference
Deduction
Analysis
L5 –
Meta-cognition
IdentifyObj
Abstraction
L4 –
Perception
Attention
L2 –
Memory
SBM
L1 –
Sensation
Vision
Emotions
STM
Audition
Decission making
Synthesis
…
Search
…
LTM
Smell
…
Memorize
L3 –
Action
…
…
ABM
CPM
Tactility
Taste
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 80
IICI*CC
The World Wide Wisdom (WWW+) Internet
Feature
Purpose
WWW
(the Internet)
Information
sharing
- Data computers
Technology - Search engines
- Communications
Protocols (IPs)
Theoretical
foundations
Data processing
Networking
WWW+
(Next generation of the Internet)
Intelligent behavior (wisdom)
generation and providing
- Cognitive computers (cCs)
- Cognitive learn engines (CLEs)
- CC protocols (CPs) /
WWW+ RTOS
Cognitive informatics
Denotational mathematics
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 81
IICI*CC
The International Consortium of WWW+
The WWW+
Network
Resear chers
(9)
Canadian
Universities (8)
Industrial
Partners (6)
Internat ional
Universities (2 )
Key
research ers
(9)
U. of Calgary
IBM Canada
UC Berkeley
U. of A lberta
Stanford Univ.
Graduate
students/
PD Fs
(40)
Oracle (Sun)
Canada
U. of T oronto
Undergrad.
Students
(5 yea rs, 100)
Engineers of
industrial
partners
(10)
TRLabs
U. of Manitoba
U. of Regina
Ryerson U.
U. of Waterloo
Indus
Automation In c.
A AI
EMRG
U. of N ew Bruns wick
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 82
IICI*CC
5. Conclusions
1. Introduction
2. Historic Development of Cognitive Informatics (CI)
3. The Latest Advances in CI
- The theoretical framework of CI
- The formal models of the brain (LRMB)
- Abstract intelligence (αI)
- Neural informatics (NeI) and the OAR mode of memory
- The nature of knowledge and learning
- Denotational mathematics (DM) for CI
4. Applications of CI
► 5. Conclusions
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 83
IICI*CC
Conclusions
• CI : Explore and explain the brain in a trans-disciplinary
approach
• CI : Shifting “Knowledge is power” to “Intelligence
(wisdom) is power”
• CI : Introducing mathematics and formal inference into
bran studies
• CI : Lead to the emergence of the next generation of IT
and cognitive computing technologies and new
industrial sectors that dramatically change our life
and perspectives to human and machine intelligence
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 84
IICI*CC
Framework of Research (I)
· Cognitive Informatics
- Natural intelligence [IEEE RAM]
- Neuroinformatics [IEEE ICCI'09]
- The layered reference model of the brain (LRMB) [IEEE TSMC(C)]
- The object-attribute-relation (OAR) mode of internal knowledge representation [IJCINI]
- The mathematical model of consciousness
- Autonomous machine learning based on concept algebra [ACM TAAS]
- Simulations of cognitive processes of the brain [to appear]
- Memory capacity of human brains [JB&M]
- Contemporary cybernetics [IEEE TSMC(B)]
- CI foundations of visual information processing [IJCINI]
- CI foundations of creativity [IJCINI]
· Cognitive Computing (CC)
- Cognitive computing [IJSSCI] [IJCINI]
- Cognitive computers that think and feel [IEEE TSMC(B)]
- Level 1: Imperative computing
- Level 2: Autonomic computing
- Level 3: Cognitive (autonomous) computing
- Denotational mathematical means for cognitive computing [TCS]
- A unified reference model of autonomous agent systems (AAS's) [IJCINI]
- Semantic computing [IJSC] [FI]
- Cognitive process of decision making [IJCINI]
- Cognitive process of problem solving [ICogSys]
- Cognitive process of memorization [TCS]
- Cognitive process of creations [IJCINI]
Framework of Research (II)
· Denotational Mathematics
- Concept algebra [IJCINI]
- System algebra [IJCINI]
- Real-time process algebra (RTPA) [IJCINI]
- Visual semantic algebra (VSA) [IJSSCI]
- Granular algebra [IEEE TSMC(A)]
- System algebra for GrC [IJCINI]
- Fuzzy qualification and quantification [to appear]
- Fuzzy causality analyses [to appear]
· Abstract Intelligence (αI) / Cognitive Robotics
- Mathematical model of αI [IJSSCI]
- The generic abstract intelligent model (GAIM) [IEEE TSMC(B)]
- Level 1: Imperative intelligence
- Level 2: Autonomic intelligence
- Level 3: Cognitive (autonomous) intelligence
- Studies on paradigms of αI (e.g. natural, artificial, machinable, computational
intelligence)
- Machinable thought [IEEE ICCI'09]
- A unified reference model of autonomous agent systems (AAS's) [IJCINI]
- Hybrid intelligence [IJSSCI]
- Machine perceptions (emotions/motivations/actions) in computational intelligence
- Abstract knowledge system [TCS]
Framework of Research (III)
· Software Science
- Theoretical software engineering [SEF]
- Mathematical laws of software engineering [TCS]
- The generic mathematical model of programs [IJSSCI]
- The unified data model (UDM or CLM) for modeling system architectures [IJSSCI]
- The unified process model (UPM) of system behaviors (RTPA) [IJCINI]
- Formal semantics of software [DS]
- Deductive semantics [IJCINI]
- The big-R notation [IJCINI]
- Operational semantics [IJSSCI]
- Denotational semantics [IJSSCI]
- Deductive grammar for NLP [FI 90(4)]
- Formal principles of software engineering [TCS]
- The coordinative work organization theory for software project organization [IJCINI]
- The formal economic model of software engineering costs (FEMSEC) [IJSSCI]
- The formal framework of software engineering measurement system (SEMS)
- Cognitive foundations of software engineering [IJSSCI]
- Cognitive complexity of software [IJSSCI]
- Autonomic software code generation (Auto-CG) [IJSSCI]
- Built-in tests (BITs) [ACM CS]
- Formal design models/frameworks of software (real-time/embedded) systems [SEF]
- The Telephone Switching System (TSS) [IJSSCI]
- The Lift Dispatching System (LDS) [IJSSCI]
- The Real-Time Operating System (RTOS+) [IJSSCI]
- The Air Traffic Control System (ATCS) [IJSSCI]
- The software engineering process reference model (SEPRM) [CRC]
Framework of Research (IV)
· System Science
- Abstract system theories [SEF]
- System algebra [IJCINI]
- System algebra for GrC [IJCINI]
- The long-life span system theory for global warming [IJNS]
- Basic laws of management science [IJCINI]
- The formal model of abstract games [ IJNS]
· Knowledge Science and Autonomous Learning Systems
- The formal knowledge system [TCS]
- Autonomous machine learning based on concept algebra [ACM TAAS]
- The AutoLearner [ ]
- The Cognitive Learning Engine (CLE) [ ]
Univ. of Vienna, Austria, Jan. 11, 2011, © Prof. Y. Wang 88
IICI*CC