The goal of AI research is the simulation and approximation of human intelligence by computers. To a large extent this comes down to the development of computational reasoning services that allow machines to solve problems. Robots are the stereotypical example: imagine what a robot needs to know before it is able to interact with the world the way we do? It needs to have a highly accurate internal representation of reality. It needs to turn perception into action, know how to reach its goals, what objects it can use to its advantage, what kinds of objects exist, etc. The field of knowledge representation (KR) tries to deal with the problems surrounding the incorporation of some body of knowledge (in whatever form) in a computer system, for the purpose of automated, intelligent reasoning. In this sense, knowledge representation is the basic research topic in AI. Any artificial intelligence is dependent on knowledge, and thus on a representation of that knowledge. The history of knowledge representation has been nothing less than turbulent. The roller coaster of promise of the 50's and 60's, the heated debates of the 70's, the decline and realism of the 80's and the ontology and knowledge management hype of the 90's each left a clear mark on contemporary knowledge representation technology and its application.

1. History of Knowledge Representation Rinke Hoekstra Universiteit van Amsterdam and Vrije Universiteit 07-12-2010 SIKS Course - Knowledge Modelling 1

2. Context About me… Knowledge Engineering Ontologies Web Ontology Language (OWL 2) Semantic Web 07-12-2010 SIKS Course - Knowledge Modelling 2

3. Overview In the beginning… (400 BC – 1900s) Scruffies vs. Neats (1970-ies) The Dark Ages (1980-ies) Engineering Revival (1990-ies) The ‘O’ Word (1995 onwards) 07-12-2010 SIKS Course - Knowledge Modelling 3

4. In the beginning… 400BC – 1900s 07-12-2010 SIKS Course - Knowledge Modelling 4

5. Aristotle (384 BC – 322 BC) Dialectics reductio ad absurdum Deduction premises  conclusion (Plato) Syllogisms Standard logic until the 19th century Categories 07-12-2010 SIKS Course - Knowledge Modelling 5

6. Syllogisms Example Major premise All mortal things die Minor premise All men are mortal things Conclusion All men die Forms Names Barbara (AAA), Celarent (EAE), … 07-12-2010 SIKS Course - Knowledge Modelling 6

7. Aristotle’s Categories Time pos. relative to events Position condition of rest (action) State condition of rest (affection) Action production of change Affection reception of change Substance primary vs. secondary Quantity extension Quality nature Relation Place position relative to environment 07-12-2010 SIKS Course - Knowledge Modelling 7

8. Porphyry of Tyre (233–c. 309) 07-12-2010 SIKS Course - Knowledge Modelling 8

9. Brentano (1838-1917) 07-12-2010 SIKS Course - Knowledge Modelling 9

10. Ramon Llull (1232 – 1315) Mechanical aids to reasoning 07-12-2010 SIKS Course - Knowledge Modelling 10

11. Scientific Revolution (17th and 18th century) Dualism René Descartes (1596 – 1650) Body as machine <-> Mind Empiricism John Locke (1632 – 1704) Royal Society Engineering Christiaan Huygens (1629 – 1695) Blaise Pascal (1623 – 1662) 07-12-2010 11 SIKS Course - Knowledge Modelling

12. John Wilkins (1614 – 1672) Universal Character Replace latin Decimal system of measure (~metric) Tree with 3 layers 07-12-2010 SIKS Course - Knowledge Modelling 12

13. Gottfried Wilhelm Leibniz (1646 – 1716) CharacteristicaUniversalis “Once the characteristic numbers of most notions are determined, the human race will have a new kind of tool, a tool that will increase the power of the mind much more than optical lenses helped our eyes, a tool that will be as far superior to microscopes or telescopes as reason is to vision.” (Leibniz, Philosophical Essays) 07-12-2010 SIKS Course - Knowledge Modelling 13

14. Calculators Pascaline Addition Substraction Stepped Reckoner Multiplication Division Binary System … but Leibniz wanted more Calculus Ratiocinator 07-12-2010 SIKS Course - Knowledge Modelling 14

15. Another Leibniz Quote "If controversies were to arise, there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in their hands, and say to each other: Let us calculate.” Leibniz, Dissertio de Arte Combinatoria, 1666 07-12-2010 SIKS Course - Knowledge Modelling 15

16. Linnaeus (1707-1778) –SystemaNaturae 07-12-2010 SIKS Course - Knowledge Modelling 16

17. … so, what’s new? Syllogisms Rules of valid reasoning Reasoning as Calculation Symbol Manipulation Categories Top-down categories of thought Universal Character/SystemaNaturae Bottom-up inventory of phenomena in reality 07-12-2010 SIKS Course - Knowledge Modelling 17

18. GottlobFrege (1884 – 1924) Logic Study of correct reasoning Arithmetics and Mathematics Begriffschrift Formal Language (of Meaning) Axiomatic Predicate Logic Calculus Variables, Functions, Quantifiers 07-12-2010 SIKS Course - Knowledge Modelling 18

19. Computers Algorithms Alan Turing (1912 – 1954) Processor/Memory Architecture Neumann JánosLajos(1903 – 1957) Automatic Theorem Proving Resolution Artificial Intelligence! But… 07-12-2010 SIKS Course - Knowledge Modelling 19

20. Theorem Proving ``… great theorem proving controversy of the late sixties …’’ (Newell, 1982) Problematic No human scale No organisation No procedures Small, theoretically hard problems 07-12-2010 SIKS Course - Knowledge Modelling 20

21. Scruffies vs. Neats 1970ies 07-12-2010 SIKS Course - Knowledge Modelling 21

22. Two Schools (1970ies and onwards) Philosophy (Neats) Clean, uniform language Knowledge derives from small set of ‘elegant’ first principles Theoretical understanding of reality Cognitive Psychology (Scruffies) Cognitively plausible language Knowledge is what’s in our heads Human intelligence and behaviour 07-12-2010 SIKS Course - Knowledge Modelling 22

23. Artificial Intelligence “. . . an entity is intelligent if it has an adequate modelof the world […], if it is clever enough to answer a wide variety of questions on the basis of this model, if it can get additional informationfrom the external world when required, and can perform such tasks in the external world as its goals demand and its physical abilities permit.” (McCarthy and Hayes, 1969, p.4) Frame Problem! 07-12-2010 SIKS Course - Knowledge Modelling 23

24. Epistemic and Heuristic Adequacy McCarthy & Hayes: Representation vs. Mechanism Epistemic Adequacy Correct representation Logic: axioms Heuristic Adequacy Correct reasoning Logic: calculus 07-12-2010 SIKS Course - Knowledge Modelling 24

25. Heuristic vs. Epistemic views in Psychology Knowledge is about the how Problem Solving  Production Systems Knowledge is about the what Natural Language Memory  Semantic Networks 07-12-2010 SIKS Course - Knowledge Modelling 25

26. Information Processing System (IPS) Computer as metaphor of the mind “the human operates as an information processing machine’’ Newell & Simon, 1972 07-12-2010 SIKS Course - Knowledge Modelling 26

27. Production Systems (1) Processor Interpreter Elementary Information Processes (EIP) Sequence of EIPs a function of symbols in memory Production Rules (Emil Post, 1943) if … then … Rule ‘fires’ if interpreter finds a match between condition and symbols in memory Sequential ≠ material implication 07-12-2010 SIKS Course - Knowledge Modelling 27

28. Production Systems (2) Adequacy? Correspondence to human reasoning Not ‘clean’ or ‘logical’ Escape limitations of theorem provers Local, rational control of problem solving Easily modifiable Drawback: Natural language? 07-12-2010 SIKS Course - Knowledge Modelling 28

29. Semantic Networks (1) Natural Language Ground lexical terms in a model of reality Semantic Memory M. Ross Quillian (1966) Associative Memory Semantic Networks Graph Based Nodes, planes and pointers subclass, modification, disjunction, conjunction, subject/object 07-12-2010 SIKS Course - Knowledge Modelling 29

30. Semantic Networks (2) 07-12-2010 SIKS Course - Knowledge Modelling 30

31. Semantic Networks (3) Adequacy? Correspondence to human memory Response time Property inheritance Extensions Named Attributes (type/token) Concepts vs. Examples (instances) Jaime Carbonell, 1970 Sprawl of variants 07-12-2010 SIKS Course - Knowledge Modelling 31

32. Frames (1) Criticism from Cognitive Science Frames, Marvin Minsky (1975) Scripts, Roger Schank (1975) Frames Larger `chunks’ of thought Situations (akin to planes) Default values 07-12-2010 SIKS Course - Knowledge Modelling 32

33. Frames (2) Frame system Related frames that share the same terminals … different viewpoints on the same situation Knowledge Reuse “Information Retrieval” Network Standard matching procedure Fixed perspective: situations, objects, processes (object-oriented design) 07-12-2010 SIKS Course - Knowledge Modelling 33

34. Semantic Networks (3) Technical problems Weak inference (inheritance) Unclear semantics “What’s in a link?”, Bill Woods (1975) “What IS-A is and isn’t”, Ron Brachman (1983) Consider the semantics of the representation itself 07-12-2010 SIKS Course - Knowledge Modelling 34

35. Frame (like) Languages Emphasis Interrelated, internallystructuredconcepts Knowledge Representation Language (KRL) Bobrow and Winograd (1976) Structured InheritanceNetworks Ron Brachman (1979) 07-12-2010 SIKS Course - Knowledge Modelling 35

36. Knowledge Representation Language (KRL) Known entity: prototype Description by reusable descriptors Descriptions by comparison to prototype + extension Modes of description: membership, relationship, role (object/event) Reasoning: Process of recognition, procedural attachments Inference mechanism determines meaning 07-12-2010 SIKS Course - Knowledge Modelling 36

37. SI Networks KL-ONE (Brachman, 1979; Brachman & Schmolze, 1985) Descriptions Role/Filler Descriptions Structural Descriptions Interpretive Attachments Role modality types: inherent, derivable, obligatory 07-12-2010 SIKS Course - Knowledge Modelling 37

38. SI-Network of an Arch 07-12-2010 SIKS Course - Knowledge Modelling 38

39. Epistemological Status Cognitive plausibility Epist. Status Relation to reality? Relation to representation language? 07-12-2010 SIKS Course - Knowledge Modelling 39

40. The Knowledge Level (Allen Newell, 1982) “… the crux for AI is that no one has been able to formulate in a reasonable way the problem of finding the good representation, so that it can be tackled by an AI system” (Newell, 1982, p.3) Computer System Level Medium System Processing Components Composition Guidelines Behavior Independent, but reducible to lower level 07-12-2010 SIKS Course - Knowledge Modelling 40

41. The Knowledge Level (2) “There exists a distinct computer systems level, lying immediately above the symbol level, which is characterised by knowledge as the medium and the principle of rationality as the law of behaviour” (Newell, 1982, p. 99) 07-12-2010 SIKS Course - Knowledge Modelling 41

42. The Knowledge Level (3) Not a stance viz. the intentional stance(Dennett, 1987) No representation at knowledge level (concepts, tasks, goals) Knowledge level = knowledge itself! Representation always at the symbol level Knowledge representation Representation of knowledge, not reality 07-12-2010 SIKS Course - Knowledge Modelling 42

43. Brachman’s Triangle Extended 07-12-2010 SIKS Course - Knowledge Modelling 43 (Hoekstra, 2009)

44. Representation and Language Brachman’s levels in Semantic Nets Primitives of KR languages Requirements neutrality, adequacy, well-defined semantics 07-12-2010 SIKS Course - Knowledge Modelling 44

45. Epistemological Level Missing level Knowledge-structuring primitives “The formal structure of conceptual units and their interrelationships as conceptual units (independent of any knowledge expressed therein) forms what could be called an epistemology.” (Brachman, 1979, p.30) Two interpretations Adequacy of Language for some level Representation at a level e.g. Logical primitives as concepts 07-12-2010 SIKS Course - Knowledge Modelling 45

46. Optimism Modern Knowledge Representation Representation of expert knowledge Performance over Plausibility Modern Languages Defined semantics Clear epistemological status 07-12-2010 SIKS Course - Knowledge Modelling 46

47. The Dark Ages 1980ies 07-12-2010 SIKS Course - Knowledge Modelling 47

48. Practical Applications (1980s) Expert Systems Production Rules Rules of thumb Relatively clear status Memory in IPS of secondary importance Severe problems Scalability Reusability 07-12-2010 SIKS Course - Knowledge Modelling 48

49. MYCIN and GUIDON (William Clancey, 1983) MYCIN: medical diagnosis GUIDON: medical tutoring “transfer back” expert knowledge Problematic No information about how the rule-base was structured: design knowledge “Compiled Knowledge” 07-12-2010 SIKS Course - Knowledge Modelling 49

50. Role of Knowledge in Problem Solving 07-12-2010 SIKS Course - Knowledge Modelling 50

51. Knowledge Types Order of rules: problem solving strategy Structure in knowledge Common causes before unusual ones Justification: domain theory Identification rules Causal rules World fact rules Domain fact rules 07-12-2010 SIKS Course - Knowledge Modelling 51

52. Convergence? Heuristic vs. Epistemological Adequacy Two approaches Different formalisms Same types of knowledge Two solutions Components (Clancey) Knowledge Structuring (Brachman) 07-12-2010 SIKS Course - Knowledge Modelling 52

53. Problems Knowledge Acquisition Bottleneck (Feigenbaum, 1980) The difficulty to correctly extract relevant knowledge from an expert into a knowledge base Interaction Problem (Bylander and Chandrasekaran, 1987) Different types of knowledge cannot be cleanly separated Problem for reuse 07-12-2010 SIKS Course - Knowledge Modelling 53

54. ENGINEERING REVIVAL 1990s 07-12-2010 SIKS Course - Knowledge Modelling 54

55. Knowledge Acquisition Ensure Quality Reuse Ad hoc methodologies Engineering Knowledge modelling vs. extraction Implementation guided by specification 07-12-2010 SIKS Course - Knowledge Modelling 55

56. CommonKADS(Wielinga et al., 1992, van Heijst et al., 1997) Knowledge Level Model Independent of KR language Solution to the KA Bottleneck? Limited Interaction Hypothesis Solution to the Interaction Problem? 07-12-2010 SIKS Course - Knowledge Modelling 56

57. Reuse Role limiting (US) Direct reuse Index symbol level representations Detailed blueprints Skeletal Models (Europe) Reuse of `understanding’ Knowledge-level ‘sketches’ Library of reusable knowledge components 07-12-2010 SIKS Course - Knowledge Modelling 57

58. Knowledge Types (1) Control Knowledge Task Knowledge Inference Knowledge Problem Solving Methods (Breuker & van de Velde, 1994) 07-12-2010 SIKS Course - Knowledge Modelling 58

59. Knowledge Types (2) Domain Knowledge Index PSM’s for reuse  Epistemology Generic domain theory What an expert system ‘knows’ about: ONTOLOGY 07-12-2010 SIKS Course - Knowledge Modelling 59

60. Functional Perspective (Hector Levesque, 1984) Descend to the Symbol Level? Knowledge Base Abstract datatype Competencies Set of TELL/ASK queries Capabilities of KB Function of queries/answers, assertions 07-12-2010 SIKS Course - Knowledge Modelling 60

61. Knowledge Based Systems Architecture Specialised KR languages Specialised Services Performance guarantees Domain Theory Identification, Classification KL-ONE like languages… Control Knowledge Rules… 07-12-2010 SIKS Course - Knowledge Modelling 61

62. The return of logic (Levesque & Brachman, 1987) Classification as logical inference Exact semantics Trade-off Expressive power Computational efficiency Restricted Language Thesis “… general purpose knowledge representation systems should restrict their languages by omitting constructs which require non-polynomial (or otherwise unacceptably long) worst-case response times for correct classification of concepts.” (Doyle & Patil, 1991) 07-12-2010 SIKS Course - Knowledge Modelling 62

63. Description Logics (Baader & Hollunder, 1991) KL-One, NIKL, KL-Two, LOOM, FL, KANDOR, KRYPTON, CLASSIC … Quest Expressive Sound & Complete Decidable KRIS, SHIQ, SHOIN, SROIQ, … 07-12-2010 SIKS Course - Knowledge Modelling 63

64. … and the rest? Domain Theory Causal Knowledge Naïve Physics Qualitative Reasoning (J. de Kleer, K.D. Forbus, B. Bredeweg, …) Strategic Knowledge Logic-based approaches Prolog, Datalog, etc.. … no principled effort. 07-12-2010 SIKS Course - Knowledge Modelling 64

65. The ‘O’ Word 1995 and onwards 07-12-2010 SIKS Course - Knowledge Modelling 65 Oh no! Not that again!

66. Pop Quiz What is an ontology? 07-12-2010 SIKS Course - Knowledge Modelling 66

67. Ontology “Ontology or the science of something and of nothing, of being and not-being of the thing and the mode of the thing, of substance and accident” G.W. Leibniz “… ontology, the science, namely, which is concerned with the more general properties of all things.” Immanuel Kant The nature of being Aristotle’s categories 07-12-2010 SIKS Course - Knowledge Modelling 67

68. Knowledge Representation (Davis, Shrobe, Szolovits, 1993) Surrogate Set of ontological commitments through language and domain theory Fragmentary theory of intelligent reasoning sanctions heuristic adequacy Medium for pragm. efficient computation way of formulating problems (Newell) Medium of human expression ``Universal Character’’(Wilkins, Leibniz, … and Stefik, 1986) 07-12-2010 SIKS Course - Knowledge Modelling 68

69. Ontology Definitions Knowledge Management An explicit (knowledge level) specification of a conceptualization (a.o. Gruber, 1994) Knowledge Representation An explicit (symbol level) specification of a conceptualisation Philosophy A formal specification of an ontological theory 07-12-2010 SIKS Course - Knowledge Modelling 69

70. The END 07-12-2010 SIKS Course - Knowledge Modelling 70