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Yingxu Wang   Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Processors
 

Yingxu Wang Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Processors

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    Yingxu Wang   Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Processors Yingxu Wang Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Processors Presentation Transcript

    • 12th Int’l Conference on Computer Science and Its Applications (ICCSA 2012) Towards the Next Generation of Cognitive Computers: Knowledge vs Data Processors vs. Yingxu Wang, PhD, Prof., PEng, FWIF, FICIC, SMIEEE, SMACM President, International Institute of Cognitive Informatics & Cognitive Computing (ICIC) Director, Director Lab for Cognitive Informatics & Cognitive Computing University of Calgary, Canada Email: yingxu@ucalgary.ca http://www.enel.ucalgary.ca/People/wangyx/ ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 1
    • 1. Introduction ► 1. Introduction 2. Cognitive informatics (CI) g ( ) 3. Denotational mathematics (DM) 4. Cognitive co pute s (cCs) Cog t e computers 5. ConclusionsICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 2
    • The Need for Computational Intelligence in Intelligent Computers • In celebrating the 100th anniversary of Turing and his pioneer work, curiosity may lead to a fundamental q question: - If more intelligent computers that think, reason, and learn may be developed? - They are known as Cognitive Computers (cCs) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 3
    • Computing Power: Speed vs. Intelligence I vc N o rma l hu ma n C omput ingintellige nce spee d 3 ye ar o ld kit s kit’sinte llige nc e A I/C I // t 1940s 1950s 1980s 2010s Computational intelligence is not merely a speed issue! ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 4
    • Abstract Intelligence (αI) α• Intelligence is a human or system ability that autonomously transfers a piece of information into a behavior: I  f :I  B• Abstract intelligence (I) g ( ) - A theory of intelligence science that studies abstract, natural, and artificial intelligence across the neural, cognitive, functional, and mathematical levels from the bottom up. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 5
    • Roles of Intelligence in Cognitive Computing The abstract world (AW ) I The natural world (NW ) I M E The physical world (PW ) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 6
    • Constraints of Classic Computers• The Turing and von Neumann machines are generic data processors created on a basic assumption that objects and behavior of any computing problem can be reduced onto th bit l t the level. l• However, there is an entire range of complex problems in the real world that may impossibly, or at least, inefficiently be reduced onto bits. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 7
    • Data Processors vs. Knowledge Processors• Is it possible to advance the classic computing theories and technologies closer to those of human brains as a natural knowledge processor that does not reason in ?• Instead of reducing every computing problem and solution onto  as in conventional data computers, the next generation of k t ti f knowledge computers k l d t known as cognitive computers need to be able to directly process human knowledge in . ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 8
    • 2. Cognitive Informatics (CI) 1. Introduction ► 2. Cognitive informatics (CI) g ( ) 3. Denotational mathematics (DM) 4. Cognitive co pute s (cCs) Cog t e computers 5. ConclusionsICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 9
    • Cognitive Informatics• 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; - Their engineering applications by cognitive computing. computing ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 10
    • Advances of Human Brain of Natural Intelligence• What make human beings as human? - Walk - Making tools - Work - Languages g g - Abstract thinking/inference capability of the brain• 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. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 11
    • Abstract Intelligence (αI) α • Abstract intelligence, I, is the universal mathematical form of intelligence that transfers information into knowledge and behaviors. k l d db h iNo. Form of intelligence g Embodying means y g1 Natural intelligence (NI) Naturally grown biological and physiological organisms2 Artificial intelligence (AI) A tifi i l i t lli Cognitively-inspired artificial models C iti l i i d tifi i l d l and man-made systems3 Machinable intelligence (MI) Complex machine and wired systems4 Computational intelligence Computational methodologies and (CoI) software systems ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 12
    • Theoretical Framework of αI Logical model Dimension of Dimension of paradigms embodying means Functional modelComputational Machinable Abstract Artificial NaturalIntelligence Intelligence Intelligence Intelligence Intelligence (I) Cognitive model Neural model ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 13
    • The Generic Abstract Intelligence Model (GAIM) K LTMStimuli Ir D B Behaviors SBM Ic ABMEnquiries Ip I Ii STM  I  I p : D  I (Perceptive) || I c : I  K ( g (Cognitive) ) || I i : I  B (Instructive) || I r : D  B ( e ect ve) (Reflective) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 14
    • The Layered Reference Model of the Brain (LRMB) (LRMB)
    • LRMB: Configuration of Processes L if e b eh a v io r s a n d c om p l e x a ct i on sL a ye r 7: T h e h i g h e r c o g n i ti v e p r o c es se s C o m p reh en s i o n L e arn in g Pr o b le m D eci s i o n C re at i o n P la n n in g Pa t te rn s o lv i n g m ak i n g re co g n i t io nL a ye r 6: M et a i n fe r en c e p r o c e ss es y D ed u c ti o n In d u ct i on A b d u ct i o n A n al o g y A n a l ys i s Sy n t h es i sL a ye r 5 : M et a co g n i ti ve p ro ce ss es O b je ct A b st r a- C on cep t C at eg o r i - C o m p a- M em or i - Q u al i fi - Q u an t i fi - Sel e ct i o n S ear ch Mode l Im a g ery Id en ti f i fy c t io n e st a b l is h . i i z at i on r i so n z at i o n c at i o n ca ti o n i es t ab l i sh . b hL a ye r 4: A c ti o n p ro ce ss es W ir ed ac ti o n s C on t in g e n t a ct i on s ( Sk i l l s) (T em p or ar y b eh av i o rs )L a ye r 3: P e r ce p ti o n p r o c es se s S el f- A t t en t i on M o t i v at i on an d E m o t i on s A tt i t u d es Se n s e o f Sen se o f C o n s ci o u s n e ss g o a l -s et t in g s p at i al i t y m ot i o nL a ye r 2: M em o r y p r o ce ss es S en s o ry b ff r bu ffe Sh o r t -t erm t L o n g - t rm te A ct i on b u ff er t M em o ry M em o r y M e m or y M em o ryL a ye r 1: S e n sa ti o n a l p r o ce ss es V i si o n A u d it i o n Sm el l T ac ti l i t y T as t e T h e p h ys i o l o g i ca l /n eu ro l o g i ca l B r ai n
    • The Abstract Intelligence Model of the Brainb n [Cerebrum] STM LTM LTM LTM (Working) (Visual) (Knowledge) (Experience/episode) [Frontal lobe] [Occipital lobe] [Temporal lobe] [Parietal lobe] Sensories Occipital O ii l Behaviors Vision lobe B-CPU [Visual area] Eyes Perception Engine ABM Action Muscle MUX drive servos Face Temporal [Thalamus] Audition lobe (attention Arms [ Auditory switch) [Primary [Pons/ [motor area] neurons] Legs [Hippo- Conscious Engine motor medulla] cortex] … Smell Parietal campus] [Hypothalamus] lobe Others Taste [Somat. area] [Pons] Touch Body stimuli CSM Survival behaviors Stimuli [Medulla] Reflective [Cerebellum] [spinal cord] actions SBM The Logical Model of the Brain (LMOB) - Wang, 2012 ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 17
    • The OAR Model of Memory and Knowledge OAR = (O, A, R) O – object A – attribute ib R – relation LTM: A hierarchical and partially connected neural clusters ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 18
    • 3. Denotational Mathematics (DM) 1. Introduction 2. Cognitive informatics (CI) g ( ) ► 3. Denotational mathematics (DM) 4. Cognitive co pute s (cCs) Cog t e computers 5. ConclusionsICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 19
    • αI is Mainly a Mathematical Entity• The lasting vigor of automata theory, Turing machines, and formal inference methodologies reveals that suitable mathematical means such as set, relations, tuples, processes, and symbolic logics are the essences of abstract and computational intelligence intelligence.• Although these profound mathematical structures underlie the modeling of natural and machine intelligence, the level of their mathematical entities is too low to be able to process concepts, knowledge, and series of behavioral processes. i fb h i l ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 20
    • Mathematical Foundations of Cognitive Computers• The problem - The computing needs for complex real-world problems may impossibly, or at least, inefficiently be reduced onto bits (). p y, , y ( ) - Most of the complex entities in the real world cannot be abstracted and represented by pure numbers in  or  (real numbers).• The finding - The computing problems are a Hyper Structure () beyond  and . -E E.g.: F Formal knowledge, abstract concepts, behavioral processes, lk l d b b h i l semantics, causations, inferences, abstract systems• The need - Denotational mathematics (DM) - Those beyond Boolean algebra and predicate logic ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 21
    • New Problems Need New Forms of Mathematics• The domain of problems in CI and αI are Hyper Structures  beyond that of pure real numbers  or bits .• The maturity of a discipline is characterized by the maturity of its mathematical means.• The requirements for reduction of complex knowledge onto the low-level data objects in conventional computing technologies and their associated analytic mathematical means have greatly constrained th inference and computing ability toward the t i d the i f d ti bilit t d th development of intelligent knowledge processors known as cognitive computers.• This has triggered the current transdisciplinary investigation into new mathematical structures for I in the category of denotational mathematics. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 22
    • Categories of Mathematics in Science & Engineering Analytic A l ti mathematics – d t th ti deterministic functions on  i i ti f ti Analytic mathematics deals with mathematical entities with accurate relations and functions. Numerical mathematics – recursive and approx. functions on  Numerical mathematics deals with mathematical entities with discrete and recursively approximate relations and functions. Denotational mathematics –Series of dynamic functions on  [HyperStructures] Denotational mathematics deals with high-level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, concepts, knowledge, processes, inferences, decisions, intelligence, and systems. Given a certain mathematical structure, when both its functions and I/O are adaptive in a series, it belongs to the category of denotational mathematics; otherwise, it falls into the category of analytic mathematics or numerical mathematics. mathematics ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 23
    • What is DM? DM?•D Denotational mathematics (DM) i a category of t ti l th ti is t f complex mathematical structures that deals with high-level mathematical entities in  beyond numbers and sets, such as abstract objects, complex relations, perceptual information, abstract concepts, knowledge, intelligent behaviors, behavioral processes, formal g , p , semantics, and systems. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 24
    • Denotational Mathematics Function Category Mathematical Means Conven- Denotational tionalIdentify objects & To be (|=) Logic  Concept algebra attributes  Semantic algebra  Visual semantic algebra (VSA)Describe relations To have (|) Set theory  System algebra & possessioniDescribe status and To do (|>) Functions  Behavioral process algebra behaviors (BPA)  Inference algebra ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 25
    • DM: A Formal Means for Solving Problems in CC• The requirements for reduction of complex knowledge onto the low level data objects in conventional low-level computing technologies and their associated analytic mathematical means have greatly constrained the inference and computing ability toward the development of intelligent knowledge processors known as cognitive computers.• This has triggered the current transdisciplinary investigation into new mathematical structures for I in th i the category of d t f denotational mathematics. t ti l th ti ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 26
    • Paradigms of DM
    • Concept Algebra Bank bo = br = bs =bank(organization) bank(river) bank(storage) Words (ambiguity) vs. Concepts (unique) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 28
    • The Generic Model of an Abstract Concept  An abstract concept c is a 5-tuple, i.e.: c A Ri Ro c  (O, A, R , R , R ) Other Cs O Other Cs c i o c Rwhere O is a nonempty set of objects of the concept, O = {o1, o2, …, om}  Þ, where Þ denotes a power set of abstract objects in the universal discourse U. A is a nonempty set of attributes, A = {a1, a2, …, an}  Þ, where Þ denotes a power set of attributes in U. Rc = O  A is a set of internal relations. Ri  C  c is a set of input relations, where C is a set of external concepts in U. Ro  c  C is a set of output relations. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 29
    • bo = bank(organization)b o S T = (A , O , R c , R i , R o ) = ( b o S T . A = { o r g a n i z a t io n , c o m p a n y , f in a n c ia l b u s i n e s s, m o n e y , d e p o s it, w it h d r a w , in v e s t, e x c h a n g e } , b o S T . O = { in t e r n a t io n a l _ b a n k , n a t io n a l _ b a n k , lo c a l_ b a n k , in v e s tm e n t_ b a n k , A T M } b o S T .R c = O  A , b o S T .R i = K  b o S T, b o S T . R o = b oS T  K ) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 30
    • br = bank(river)b r S T = ( A , O , R c , R i, R o ) = ( b rS T.A = {s id e s o f a r iv e r , r a is e d g r o u n d , a p i le o f e a r th , lo c a ti o n } , b r S T.O = { r iv e r _ b a n k , la k e _ b a n k , c a n a l_ b a n k } b r S T.R = O  A , c b r S T.R = K  b r S T , i b rS T o = b rS T  K T.R ) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 31
    • bs = bank(storage)b s S T = ( A , O , R c , R i, R o ) = ( b s S T .A = { s to r a g e , c o n ta i n e r , p l a c e , o r ga n iz a ti o n }, b s S T .O = { in f o r m a ti o n _ b a n k , r e s o u r c e _ b a n k , b lo o d _ b a n k } b s S T .R c = O  A , b s S T .R i = K  b s S T, b s S T .R o = b s S T  K ) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 32
    • Knowledge Representation in Concept Algebra c3 pen printer Knowledge c1 c2 level (K) stationery O1 O2 fountainballpoint o11 o12 o13 o21 o22 Object level laser (U) brush b h Ink-jet I kj t A2 A1 a1 a2 a3 a4 A5 A6 … A7 Attribute level (M) a writing using having with an ink a printing using with a toner tool ink a nib container tool papers cartridge
    • Concept Algebra Concept Algebra CA= (C, OP, ) (Wang, 2006) = ({O, A, R , R , R }, {r , p , c}, ) c i o Concept Algebra Operation Operator C1HS, C2HS  Related   RelatedBL C1HS, C2HS  Independent   IndependentBL C1HS, C2HS  Superconcept   SuperconceptBL Relational C1HS, C2HS  Subconcept   SubconceptBL Operations C1HS, C2HS   ( r ) Equivalent = EquivalentBL C1HS, C2HS  Consistent   ConsistentBL C1HS C2HS HS,  Comparison ~  DegreeOfSimilarityBL C1HS, C2HS  Definition   DefinedBL C1HS  Inheritance   C2HS C1HS  Tailoring   C2HS Compositional + Operations C1HS  Extension   C2HS ( p ) C1HS  Substitute    C2HS c1HS  Instantiation   o1HS C1HS, C2HS, …, CnHS  Composition   CHS Compositional CHS    C1HS, C2HS, …, CnHS Operations Decomposition C1HS, C2HS, …, CnHS    Chs (c ) Aggregation cHS  Specification   C1HS, C2HS, …, CnHS ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 34
    • E.g. Equivalence and Comparison Operations c1  c2  ( A1  A2 )  (O1  O2 ) ˆ | A1  A 2 | c1 ~ c 2  | A1  A 2 |  0, c1  c 2 1 , c1 = c 2   | A2 | =  , c1  c 2  | A1 |  | A1 |  , c1  c 2  | A2 | ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 35
    • E.g. Concept CompositionICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 36
    • The Mathematical Model of Memory/Knowledge• Th abstract object, k The b t t bj t knowledge K, i the brain is a l d K in th b i i perceptive representation of information by a function rk that maps a given concept C0 into all related concepts, i.e.: n K  rk : C0  ( XC ), r  R i k i =1• The entire knowledge K is represented by a concept network, which is a hierarchical network of concepts t k hi h i hi hi l t k f t interlinked by the set of nine associations  defined in concept algebra, i.e.: n n K =  : XCi  XC j i=1 j=1 ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 37
    • 4. Cognitive Computers (cCs) (cCs) 1. Introduction 2. Cognitive informatics (CI) g ( ) 3. Denotational mathematics (DM) ► 4. Cognitive computers (cCs) g p ( ) 5. ConclusionsICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 38
    • Cognitive Computing: Toward Machines that Learn and Think• Cognitive Computing (CC) is an emerging paradigm of intelligent computing methodologies and systems that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain.• CC is developed based on the trans-disciplinary research in cognitive informatics and abstract intelligence. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 39
    • Cognitive Computers (cCs) (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. processing• cCs are able to embody machinable intelligence such as computational inferences, causal analyses, f knowledge manipulation, learning, and problem solving. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 40
    • CI Foundations for cCs• The theoretical framework of cognitive informatics [Wang 2002/07]• Information-Matter-Energy-Intelligence (IME-I) model [Wang 2002/06]• The Layered Reference Model of the Brain (LRMB) [Wang et al. 2006]• The Object-Attribute-Relation (OAR) model of knowledge representation in the brain [Wang 2003/07]• The cognitive informatics model of the brain [Wang, 2003]• The computational intelligence model of the brain [Wang, 2003]• Abstract Intelligence (I) [Wang 2007]• Neuroinformatics [Wang 2003]• Th l i l/f ti l models of the brain (LMOB/FMOB) [W The logical/functional d l f th b i [Wang 2012]• The Cognitive Reference Model of Autonomous Agent Systems [Wang 2008] ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 41
    • Denotational Mathematical Foundations of cCs• Because the basic unit of knowledge is an abstract concept in , the mathematical model of knowledge is a Cartesian product of power sets of formal concepts concepts. n n K =  : XCi  XC j i=1 j=1• The mathematical foundations of classic data computers are Boolean algebra and its logical counterparts in  .• The mathematical foundations of cognitive computers are based on co te po a y de otat o a at e at cs ( contemporary denotational mathematics (DMs) such as concept s) suc co cept algebra, inference algebra, semantic algebra and process algebra in  for rigorously modeling and manipulating knowledge, perception, leaning and inferences. ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 42
    • Abstract Intelligence (αI) Foundations of cCs αB n [Ce reb rum] STM LT M LTM LTM ( Wor king) ( Visua l) (K no wle dge) (Expe rienc e/ep isode ) [Fr ontal lo b e] [O ccip ital lo b e] [T emp or al l ob e] [ Pa rietal l ob e] Se nsori es Occ ipital Beh avi ors V isio n lobe B- CPU [ Vi sual a rea] E ye s Pe rc ep tion E ngine AB M Act ion M u sc le M UX d rive se rvos F ac e Temp oral (a ttentio n [ Th ala mus] Auditio n lobe Ar ms [ Auditory sw itc h) [Primar y [P on s/ [ moto r ar ea] mo to r medulla ] neurons ] Legs [ [Hi pp o- Con sc ious En gine … Pa i t P rieta l corte x] S me ll ca mp us] [ Hypoth ala mus] Othe rs lobe [S omat. Ta ste are a] [Po ns] To uch Body stimu li CSM Su rvival b eh aviors S timuli [ Me d lla ] du Refle ctive [Ce re b ell um] C [sp in al co rd] S BM ac tion s
    • The Architectural Model of Cognitive Computers• A cognitive computer (cC) is a category of intelligent computers that think, perceive, learn, and reason. p ,p , , - cCs: knowledge processors - von Neumann computers: data processors• The architectural model of cCs cC = AIE || CLE || SPE || FKB (CN) - AIE: autonomous inference engine - CLE: cognitive learning engine - SPE: sensory perception engine - FKB: formal knowledge base - CN: concept network ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 44
    • The CPU of Cognitive Computers Facet Conventional Cognitive computers Computers (DC) (CC)Objects Bits  Concepts (Formal knowledge)  Data Causations SemanticsBasic Logic Concept identificationoperations Arithmetic Semantic analyses Functional Behavioral processesAdvanced Algorithms Concept formulationoperations Processes Knowledge representation Programs Comprehension Learning L i The Inferences Causal reasoning Cognitive C U CPU ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 45
    • The Behavioral Spaces of Cognitive Computers Cognitive Machine CS Human behaviors behaviors Autonomic CS Imperative CS B I = {B e , B t , B int } B A = { B g, B d}  B I B C={B p, B in f}  B I  B A ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 46
    • The Layered Reference Model of the Brain (LRMB) - Wang et al., 2006 (LRMB) 2006
    • The Cognitive Learning Engine (CLE) Internal knowledge representation g p Language Knowledge Knowledge Knowledge KnowledgeInformation knowledgebase capturer analyzer integrator presenter Knowledge input (WordNet) output Conceptual Logical knowledge knowledge (Concept) representation representation OAR/DCN Physical visualization (sOAR) (OAR) knowledgebase (DCN) Relational knowledge Memory manager manipulator (CN updating) Concept formulator Compositional knowledge Knowledge retriever manipulator (Queries) The kernel of CLE ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 48
    • Cognitive Computing Based on Concept Algebra (1/3) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 49
    • Cognitive Computing Based on Concept Algebra (2/3) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 50
    • Cognitive Computing Based on Concept Algebra (3/3) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 51
    • Final Result of Leaning by cCs  C g C ’S T = C gC S T  IC S T  K PS T = ( C g C ’S T A = { C g C S T A  IC S T A  K PS T A } S T.A T. T.A T.A }, C g C ’S T .O = C g C S T .O  IC S T .O  K PS T.O , C g C ’S T .R c = O  A , C g C ’S T .R i = O A R  C g C ’ , S R C g C ’S T .R o = C gC ’  O A R ) ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 52
    • Advantages of CLE in cCs• Learn common or professional knowledge faster than human does• Learn and process knowledge continually beyond the natural memory creation constraints of humans y• 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 re learning because they use the same knowledge representation model and manipulation mechanisms ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 53
    • 5. Conclusions 1. Introduction 2. Cognitive informatics (CI) g ( ) 3. Denotational mathematics (DM) 4. Cognitive co pute s (cCs) Cog t e computers ► 5. ConclusionsICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 54
    • Conclusions •C Cognitive informatics (C ) f (CI) - Abstract intelligence (αI) - The Generic Abstract Intelligence Mode (GAIM) - The Layered Reference Model of the Brain (LRMB) - The Logical Model of the Brain (LMOB) • Denotational mathematics (DM) - Extension of the computing domain from  to  - Concept algebra - System algebra - Behavioral process algebra (BPA) - Inference algebra - Visual semantic algebra (VSA) • Cognitive computers (cCs) g p ( ) - The CI foundations of cCs - The DM foundations of cCs - The αI foundations of cCs - cCs: architecture CPU behaviors and CLE architecture, CPU, behaviors,ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 55
    • Application Areas of cCs• A wide range of applications of cC & CI have been g pp identified such as: - eBrain - Cognitive networks for collective computational intelligence - Cognitive robots - Autonomous agent networks g - Cognitive learning engines - Distributed cognitive sensor networks - Cognitive inference engine - Cognitive Internet and WWW+ ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 56
    • Cognitive Robots - IEEE Robotics & Automation Wang, 2011 ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 57
    • ICIC
    • The International eBrain Consortium T h e eB ra in C o ns o r ti u mR e s ea r c h e r s C a n ad i an I n d u st r ia l I nt er na t i on a l ( 9) U n iv e r s it i es ( 8 ) P a r t n er s ( 6 ) U n iv e rs i ti e s (2 ) K ey U . o f C a l ga r y IB M C a na da U C B e r k e le y r es e ar ch er s (9) U . o f A lb e r t a O r a cl e ( S u n ) S ta n fo r d U n i v . Ca na da G r a d u at e U . o f T or o n to s t ude nt s / T R L a bs P D Fs (4 0) U . o f M a n i t ob a In du s U nde rgra d . A u to m at i on In c. S tu d e n t s U . o f R eg in a ( 5 y e a rs , 10 0 ) A A I R y er so n U . E n g in e e rs of EM R G i n d u s tr i al U . o f W a t e r lo o p ar t n e r s (10 ) U . o f N ew B r u n s w ic k ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 59
    • IEEE ICCI*CC 2012