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Chapter 5 Knowledge Representation 知識表示法

Chapter 5 Knowledge Representation 知識表示法. Knowledge （知識） ＋ Inference （推論） ＝ Expert Systems （專家系統） Affect the development, efficiency, speed, and maintenance of expert systems epistemology: concerned with the nature, structure, and origin of knowledge

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Chapter 5 Knowledge Representation 知識表示法

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  1. Chapter5 Knowledge Representation 知識表示法 Expert Systems sstseng

  2. Knowledge（知識） ＋ Inference （推論）＝ Expert Systems（專家系統） • Affect the development, efficiency, speed, and maintenance of • expert systems • epistemology: concerned with the nature, structure, and origin of knowledge • a priori comes from the Latin and means “That which precedes” 5.1 The meaning of Knowledge（知識） Epistemology(認知論) Philosophic Theories ARISTOTLE PLATO LOCKE MILL A Priori Knowledge e.g. all triangles have 180 degrees (considered to be universally true) A posteriori Knowledge e.g. the light is green • a posteriori knowledge can be verified using • sense experience Expert Systems sstseng

  3. Classifications of knowledge（知識） • Procedural Knowledge（程序性知識） • How to do something • Declarative Knowledge（陳述性知識） • The truth of something • 　　“Don’t put your fingers in a pot of boiling water” • Tacit Knowledge（隱含知識） • (Unconscious Knowledge) • Cannot be expressed explicitly • －An example is how to move your hand • －Walking or riding a bicycle • －ANS is related to tacit knowledge Expert Systems sstseng

  4. Analogy to Wirth’s classic expression • Algorithms + data structures = programs • Knowledge + Inference = expert systems Expert Systems sstseng

  5. Levels Meta Knowledge (rules about rules) Knowledge (rules+facts) Information (facts) Data Noise Expert Systems sstseng

  6. The sequence of 12 numbers：137178007124 Without knowledge. This entire sequence may appear to be noise. Rule 1：IF Rain THEN Bring Raincoat Rule 2：IF Rain THEN Bring Umbrella Meta Rule 1：Try Rule 2 First Meta Rule 2：IF Ride a motorcycle THEN Try Rule 1 First Meta knowledge is knowledge about knowledge and expertise. －would specify which knowledge base was applicable. Expert Systems sstseng

  7. Representation（表示法） • Backus - Naur form • Ontology（本體論） • Semantic Network（語意網路） • Frames-based Knowledge（框架式知識） • Case-based Knowledge（案例式知識） • Rule-based Knowledge（規則式知識） • Knowledge Object（知識物件） • Logic（邏輯） Expert Systems sstseng

  8. This notation is a meta language for defining the • syntax of a language • Define the syntax of a language • e.g. • <sentence>::＝ <subject><verb><end-mark> • <subject>::＝ I ｜ You｜We • <verb> ::＝ left ｜came • <end-mark> ::＝ . ｜ ?｜！ • Parse Tree (derivation tree) • sentence • subject verb end-mark • You came ? 5.2 Backus - Naur form ( BNF ) Expert Systems sstseng

  9. 5.3 Ontology（本體論） • Ontology一詞在90年代就開始被使用在人工智慧領域，描述知識的知識構成要素之間的關係。 • Ontology的研究大致上可略為分為兩個方向： • 針對特定的問題領域建立大量的Ontology • 例如：建立某些領域詞彙的Ontology • 研究Ontology的建構方法與表示方法 • 例如：利用XML（可延伸標記語言）或是RDF（資源描述格式） Expert Systems sstseng

  10. Ontology的發展主要是用來使知識分享和再用更為容易。Ontology的發展主要是用來使知識分享和再用更為容易。 • 不同的研究對於Ontology的表示與描述有不同的方法，目前還未看到較一般化、通用的表示法 。 • 範例：使用RDF來描述適性化教材的Ontology Expert Systems sstseng

  11. 5.4 Semantic Network（語意網路） (Quillian 67 & 68) • A classic AI representation technique used for • propositional information is sometimes called • Propositional Net • A proposition(命題) is a statement that is either true or false • A directed graph（有向圖形） • Node（點） : 知識的組成元素或是種類 • Arc（有向線段） : 知識組成元素間的關 • 　「is a」 • 「a kind of」 Expert Systems sstseng

  12. General Net San Francisco Chicago New York Indianapolis Los Angeles Houston Expert Systems sstseng

  13. A Semantic Net（語意網路） sister-of Carol David wife-of wife-of Ann Mark husband-of husband-of Father- of Mother- of father-of Mother-of wife-of Tom Susan husband-of father-of Mother-of John Expert Systems sstseng

  14. 「is a」 and 「a kind of」 • 「is a」 : • 在Tail（有向線段尾段）所表示的知識物件屬 於Head（有向線段頭段）的知識類別中的一 個例子。 • 　「a kind of」(AKO) : • 在Tail（有向線段尾段）的知識類別屬Head （有向線段頭段）所表示的知識類別。 • Superclass（父類別） and Subclass（子類別） • Attribute, Value, Property • Inheritance（繼承） Expert Systems sstseng

  15. 鴿子 鳥 動物 is a AKO has-property 飛 A Semantic Network with 「is a」 and 「a kind of」(AKO) Links Expert Systems sstseng

  16. aircraft AKO AKO AKO round balloon Propeller driven jet has-shape AKO AKO AKO AKO AKO AKO has-shape blimp Concorde ellipsoidal special DC-3 DC-9 is a is a is a Goodyear Blimp Spirit of St. Louis Air Force 1 A Semantic Network with 「is a」 and 「a kind of」(AKO) Links Expert Systems sstseng

  17. 5.5 PROLOG and Semantic Nets（語意網路） • Essentials(本質、要素) of PROLOG Each of the statements above is a PROLOG predicate(述部) expression, or simply a predicate. Color(red).; red is a color father_of(Tom,John).; Tom is the father of John mother_of(Susan,John).; Susan is the mother of John parents(Tom,Susan,John).; Tom and Susan are parents of John Expert Systems sstseng

  18. Predicates can also be expressed with relations such as the IS-A and HAS-A. is_a (red,color). has_a (John,father). has_a (John,mother). has_a (John,parents). Some additional predicates is_a (Tom,father). is_a (Susan,mother). is_a (Tom,parent). is_a (Susan,parent). Expert Systems sstseng

  19. Programs in PROLOG consist of facts and rules in the general form of goals. p:-p1,p2…pn. In which p is the rule’s head and the pi are the subgoals. The symbol,:-, is interpreted as an IF. parent (x,y) : - father (x,y). parent (x,y) : - mother(x,y). grandparent(x,y) :- parent (x,z) ,parent(z,y). and an ancestor can be defined as: (1) ancestor(x,y) :- parent(x,y). (2) ancestor(x,y) :- ancestor(x,z),ancestor(z,y). Expert Systems sstseng

  20. Predicate Database (Rules and Facts) Queries Answers Interpreter User General Organization of a PROLOG System Expert Systems sstseng

  21. 　(3) parent (Ann,Mary). (4) parent (Ann,Susan). (5) parent (Mary,Bob). (6) parent (Susan,John). As another example, suppose the query is :-ancestor(Ann,John). The first ancestor rule(1) matches and X is set to Ann and Y is set to John. PROLOG now tries to match the body of (1), parent (Ann,John) with every parent statement. Expert Systems sstseng

  22. (1) is not true, the head cannot be true. Because(1) cannot be true, PROLOG then tries the second ancestor statement (2) X is set to Ann and Y is set to John. Control structure of PROLOG is of the Markov algorithm type, in which searching for pattern matching is normally determined by the order in which the Horn clauses are entered. Expert Systems sstseng

  23. 5.6 Schema (plural schemas or schematas) • A semantic net（語意網路） is an example of a shallow knowledge（淺層知識） structure. • A general term to describe a complex knowledge structure • Focus on only relevant(有意義的) knowledge • For examples：FRAME,SCRIPT Expert Systems sstseng

  24. 5.7 Frames-based Knowledge（框架式知識）(Minsky 75) • Suitable for related knowledge about a narrow subject with much default knowledge • script－a time-ordered sequence of frames • Slot(槽)：Attribute(屬性) Slot 值：Value • Example a car frame Expert Systems sstseng

  25. Slot 值 • Some frame-based tools (KEE) allow a wide range of items to be stored in slots • an assigned value .a default value • Rules .graphics • Comments .debugging information • questions for users .function • procedural attachment .to other frame Expert Systems sstseng

  26. Procedural Attachment • If – needed, if-added, if-removed • Examples：Human Property Expert Systems sstseng

  27. Hierarchy Expert Systems sstseng

  28. FRAMES School meeting A KIND OF Weekly Meeting Occasional Meeting Monthly Meeting IF-ADDED:inform the participants IF-REMOVED: inform the participants IS A IF-ADDED:inform the person IF-REMOVED: inform the person IF-CHANGED:... Expert Systems sstseng

  29. Difficulties with FRAMES（框架） name elephant specialization of a-kind-of mammal color gray legs4 trunk a cylinder • Stereotype is that it have well defined features so that many of its slots have default values Three-legged, two-legged • Most frame（框架） systems do not? • Provide a way of defining unalterable slots • Nothing is really certain is such a unrestrained system Expert Systems sstseng

  30. 1.Centers L 2.C begins looking around Shopping Script： C(customer),S(salesperson) M(merchandise),D(dollars) L(a store) 3. C looks for a specific M 4. C looks for any interesting M 5. C asks S for help 6. 8. C fails to find M 7.C finds M’ 9. C leaves L 12. goto step 2 10.C buys M’ 11.C leaves L 13.C leaves L 14.C takes M’ Expert Systems sstseng

  31. Did Mary buy anything? Mary went shopping for a new coat. She found a new one. She really liked When she got it home, and discovered that it went perfectly with her favorite dress . Question：Did Mary buy anything? Expert Systems sstseng

  32. 5.8 Case-based Knowledge（案例式知識） • 通常是用來描述屬於經驗的知識 • 從過去的經驗中，判定是何種相似的case（案例），並且依據過去解決此問題的方法，來解決此次問題 • Case（案例） : • 案例名稱 • 屬性 • 屬性值 Expert Systems sstseng

  33. 利用Case-based Knowledge（案例式知識）建構Expert System（專家系統） • Case Retrieve（案例擷取） • Case Reuse（案例再用） • Case Revise（案例修正） • Case Retain（案例更新） Expert Systems sstseng

  34. 案例推論循環 Expert Systems sstseng

  35. 5.9 Rule-based Knowledge（規則式知識） • 知識領域具備需要推論的特性 • 例如：醫生依據其所學的醫學知識及病人所呈現的症狀去判別所罹患的疾病 • 最基本的Rules（規則）形式 如果 「狀態」 則 「結論」 IF (condition) THEN (conclusion) • Inference Chaining（推論鏈） • Forward Inference（前向推論） • Backward Inference（後向推論） Expert Systems sstseng

  36. 5.10 Knowledge Object（知識物件） • Object Oriented（物件導向） • Class（類別） and Object（物件） • Super-class（父類別） and Sub-class（子類別） • Inheritance（繼承）、Encapsulation（封裝）、Polymorphism（多型） • Knowledge Object（知識物件） • Object-Attribute-Value Triples ( OAV )（物件-屬性-屬性值法） • 物件導向規則庫管理系統 • Knowledge Object Model（知識物件模型） Expert Systems sstseng

  37. Object-Attribute-Value Triples ( OAV )（物件-屬性-屬性值法） • OAV can be used to characterizes all the knowledge（知識） in a semantic net（語意網路） and was used in MYCIN for diagnosing infections diseases • Especially useful for representing facts（事實） • for only a single object： only attribute-value pairs (AV) Expert Systems sstseng

  38. Object-Attribute-Value Triples（物件-屬性-屬性值法） Car Wheel: 4 Function:run Object Attribute Value AKO AKO Door:3 . . . Door:4 . . . AKO AKO Carry:people size:small Carry:goods size:big AKO AKO price:$$$ Civic R9 price:$$$ price:$$$ price:$$$ ‧‧‧‧‧‧ ‧‧‧‧‧‧ is a is a year:1992 owner:crt year:1988 owner:gjh Expert Systems sstseng

  39. Limitations • Lack of standard names for links and nodes（點） • Combinatorial explosion of searching nodes（點） • Logically inadequate no “for all”, “there exist”... • Heuristically inadequate no effective search heuristics Expert Systems sstseng

  40. 物件導向規則庫管理系統 • 將規則集合與物件導向概念結合 • Rule Class（規則類別） and Rule Object（規則物件） Expert Systems sstseng

  41. 5.11 Logic（邏輯） • A description team for logic programming and expert systems is automated reasoning systems • Syllogism（三段論）: The oldest and one of the simplest types of formal logic premise: all men are mortal premise: Socrates is a man Conclusion: Socrates is mortal • Propositional Logic（命題邏輯） －a symbolic logic for manipulating propositions • First－Order Predicate Logic（第一層敘述邏輯） • Fuzzy Logic（模糊邏輯） Expert Systems sstseng

  42. Propositional Logic（命題邏輯） • Proposition • A sentence whose truth value can be determined • e.g. it is raining • Feather ( Albatrass) • Compound Statement • e.g. • If it is raining then carry an umbrella Expert Systems sstseng

  43. Tautology A compound statement that is always true e.g. P  ~p • Contradiction A compound statement that is always false e.g. P ~p • Contingent Statement neither tautology nor contradiction e.g. P Expert Systems sstseng

  44. 5.12 First - Order Predicate Logic（第一層敘述邏輯） • Propositional logic（命題邏輯） is a subset of predicate logic（敘述邏輯） • The basis of logic programming languages e.g. Prolog • Addition: Variable • universal quantifier: For all • existential quantifier: There exist e.g. (x) (x is a triangle x is a polygon) Expert Systems sstseng

  45. Limitations of Predicate Logic（敘述邏輯） • The following statement can’t be expressed in predicate logic（敘述邏輯）: • Most of the class received As • To implement Most, a logic must provide some predicates for counting, e.g. fuzzy logic（模糊邏輯）. Expert Systems sstseng

  46. 知識表示法 • 邏輯（Logic） • 　語言正規而簡單（formal syntax） • 　嚴密的理論 • 　完整的推理法則（rules of inference） • 　可證性 • 　彈性大 • 模組性高（modularity） • 不容易表示有處理（processing）和控制（control）的 　知識 Expert Systems sstseng

  47. 缺乏結構性 • 有時不太自然 • 解釋不易 　採用解析原則（resolution principles）的邏輯 　系統解不易 　事實量大時，法則的選取（rule selection）會 　有組合膨脹（combinatorial explosion）的現象 Expert Systems sstseng

  48. Exercise • 1. Draw an action frame system explaining what to do in case of hardware failure for your computer system. Consider disk crash , power supply , CPU, and memory problems. • 2. Determine whether the following are valid or invalid arguments. • A) pq, ~qr, r; ∴ p • B)~pq, p(r  s),s q; ∴ q  r • C) p (q r), q; ∴ p r Expert Systems sstseng

  49. 專題進度規劃 • 10/18 交分組名單（每組最多四人） • 11/01 交專題題目與初步構想書 • 11/29 交專題計劃書 • 12/20~1/10 上台presentation（每組十 五分鐘） • 01/17 系統Demo & 交期末專題報告 Expert Systems sstseng

  50. 誰殺了仙道？(1) • 神奈川縣高中籃球決賽前夕，陵南高中籃球隊中的明星選手——仙道彰被殺死於自家住處。警方稍後拘捕了五名嫌犯。他們是湘北高中籃球隊的櫻木花道、三井壽、流川楓、赤木剛憲、宮城良田。在警方的盤問中，他們各人都要回答四個問題。但是由安西教練側面瞭解，各嫌犯所答的四個問題中有三個答案是真話，另一個答案是假話。而且兇手就是五人其中之一。以下是五人的供詞： Expert Systems sstseng

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