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REPRESENTASI PENGETAHUAN

REPRESENTASI PENGETAHUAN. KULIAH KE-4 SISTEM PAKAR AZIZ KUSTIYO DEPARTEMEN ILMU KOMPUTER FMIPA IPB. PROCEDURAL, DECLARATIVE, AND TACIT KNOWLEDGE. Procedural knowledge  knowing how to do something. Knowing how to boil a pot of water

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REPRESENTASI PENGETAHUAN

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  1. REPRESENTASI PENGETAHUAN KULIAH KE-4 SISTEM PAKAR AZIZ KUSTIYO DEPARTEMEN ILMU KOMPUTER FMIPA IPB

  2. PROCEDURAL, DECLARATIVE, AND TACIT KNOWLEDGE Procedural knowledge knowing how to do something. Knowing how to boil a pot of water Declarative knowledge knowing that something is true or false “don’t put your finger in a pot of boiling water” Tacit or unconscious (bawah sadar) knowledge cannot be expressed by language Knowing how to move your hand

  3. Inferencing vs reasoning • Knowledge is of primary importance in expert systems knowledge + inference = expert system • inferencing  generally used for mechanical system such as expert systems • Reasoning  generally used in human thinking

  4. The Knowledge Hierarchy knowledge on knowledge (e.g how/when to apply) Rules activated by facts to produce new facts or conclusions meta- knowledge knowledge information data noise Processed data that are of interest Items of potential interest Items that are of little interest and obscure data

  5. Expert systems… • Facts = data or information • Expert systems : • draw inference using data or information • Separate data from noise • Transform data to information • Transform information to knowledge

  6. Noise vs data vs information vs knowledge • 137178766832525156430015 • Noise • Data  if meaningful • Processes data  information • Group the number by twos 13 71 78 76 68 32 52 51 56 43 00 15 • Ignore any two digit number less than 32 71 78 76 68 32 52 51 56 43 • Substitute the ASCII characters for the two-digit numbers  ?????

  7. Noise vs data vs information vs knowledge.. • Information = GOLD 438+ • Knowledge: gold is less than 500 and the price is rising • Rule: IF gold is less than 500 and the price is rising ( + ) THEN buy gold

  8. Data & Information Knowledge Inference An Example of Knowledge What’s your examples?

  9. Expertise… • Expertise is a specialized type of knowledge that experts have • Expertise is not commonly found in public sources of information (book and paper) • Expertise is implisit knowledge of the experts that must be extracted and made explicit  can be encoded in ES

  10. Wisdom… • In philosophical sense, wisdom is the peak of all knowledge • Wisdom is metaknowledge of determining the best goals of life and how to obtain them

  11. The Problem Of Knowledge Representation • The objective of research into intelligent machines is to produce systems which can reason with available knowledge and so behave intelligently. • One of the major issues then is how to incorporate knowledge into these systems • How is the whole abstract concept of knowledge reduced into forms which can be written into a computers memory. • This is called the problem of Knowledge Representation.

  12. Knowledge Representation Include: • Semantic Nets • Object-Attribute-Value Triples (OAV Triples) • Frames • Production Rules • Logic • “Ontology” Knowledge

  13. has-a tail Rabbit 1. Semantic Nets • Directed graph representation of declarative knowledge, represented by objects and binary relationships on objects • Graphical depictions • Nodes/object and arc/links • Hierarchical relationships between concepts • Reflects inheritance Node arc node

  14. Semantic Network Techniques (Cont.) Wings HAS Canary IS-A Bird TRAVEL Fly Computer Science FMIPA IPB Yeni Herdiyeni 2008 15

  15. Expanded Semantic Network  Inheritance Wings Air HAS BREATHE Canary Tweety IS-A IS-A Animal Bird IS-A IS-A TRAVEL Penguin Fly TRAVEL Walk Computer Science FMIPA IPB Yeni Herdiyeni 2008 16

  16. Semantic Network Operation (Cont.) How about penguin !?? That’s a problem with inheritance, TRAVEL – FLY  Exception Handling Computer Science FMIPA IPB Yeni Herdiyeni 2008 17

  17. 2.Object-Attribute-Value (OAV)Triples • Object dapat berupa bentuk fisik atau konsep • Atribut adalah karakteristik atau sifat dari object tersebut. • Values (Nilai) – besaran/nilai/takaran spesifik dari atribut tersebut • pada situasi tertentu. Dapat berupa numerik, string atau boolean. • Sebuah object bisa memiliki beberapa atribut --> OAV Multi-atribut • Sebuah atribut dapat dianggap sebagai suatu object baru dan memiliki atribut sendiri. • Digunakan juga pada frames dan Jaringan semantik

  18. 2.Object-Attribute-Value (OAV)Triples Facts  Proposition Proposition : A statement that is either true or false An O-A-V is a more complex type of proposition Ex :The ball’s color is red Object  The ball Attribute  Color Value  Red Computer Science FMIPA IPB Yeni Herdiyeni 2008 19

  19. Single Versus Multiple-Valued facts Ball Color Red Diameter 1 Foot Weight 1 Pound Ball Color Red Single Value Multiple Value Computer Science FMIPA IPB Yeni Herdiyeni 2008 20

  20. Single Versus Multiple-Valued Facts (Cont.) Single Value Q : Please tell me if the barometric pressure is Falling Steady Rising A : Falling Multiple Value Q : Please select the level of education High School College Graduate School A : High School College Computer Science FMIPA IPB Yeni Herdiyeni 2008 21

  21. O-A-V : Uncertainty Facts +1.0 0 - 1.0 - .3 - .6 - .3 + .6 Unknown Definitely False Probably false Probably true Definitely True Weather Forecast Rain 0.6 Object Attribute Value CF “It probably will rain today” Computer Science FMIPA IPB Yeni Herdiyeni 2008 22

  22. O-A-V : Fuzzy Facts • “The person is tall” • Humans have little difficulty in interpreting and reasoning with ambiguous terms  fuzzy Logic Height Short Medium Tall 1 Member ship Value 0.5 0 4 5 6 7 Height in Feet Computer Science FMIPA IPB Yeni Herdiyeni 2008 23

  23. Object-Attribute-Value (OAV)…

  24. 3. Frames • All knowledge about object • Hierarchical structure allows for inheritance • Allows for diagnosis of knowledge independence • Object-oriented programming • Knowledge organized by characteristics and attributes • Slots • Subslots/facets • Parents are general attributes • Instantiated to children • Often combined with production rules

  25. Structure of frame (1) • Frame: printer • superset: office-machine • subset: {laser-printer, ink-jet-printer} • energy-source: wall-outlet • maker: Epson • date: 1-April-2003 Frame name slot: value , value, …… . . . slot: facet: value, value, …… facet: value, value, ……

  26. Class and instance frames • (frame) instance: representing ”lowest-level” object; a single object or entity • (frame) class: describes different frames (either instances or classes) • every instance has an “is-a” link, pointing to its class • possibly more than one “is-a”

  27. Frames (Cont.) Object 1 IS-A Object 2 Frame Name Object 1 Class Object 2 Properties Property 1 Value 1 Property 2 Value 2 Property 3 Value 3 Computer Science FMIPA IPB Yeni Herdiyeni 2008 28

  28. Frames : Bird Frame (Cont.) Frame Name Bird Properties Color UnKnown No Wings 2 Flies True Computer Science FMIPA IPB Yeni Herdiyeni 2008 29

  29. Instance Frame : Tweety Frame (Cont.) Frame Name Tweety Class Bird Properties Color Yellow No Wings 1 Flies False Computer Science FMIPA IPB Yeni Herdiyeni 2008 30

  30. Example of frames… Panda Jenny Vicky Bamboo Type: Animal Colour: Black and white Food: EatFunc: …….. Name: Jenny Height: 1.6 Age: 5 Sibling: Name: Vicky Height: 0.7 Age: 1 Sibling: Type: Plant GrowFunc: …….. Location: Height: 2 Name: Height: Age: 0 Sibling

  31. Knowledge Representation 4:Production Rules • Grammar the complete set of production rules in a formal system • e.g. Simple grammar: <subject> -> I | You | We <verb> -> left | came <end-mark> -> . | ? | ! Possible sentences in the language, the productions, can be produced as following: I left. I left? I left! You left. You left? You left! We left. We left? We left! …

  32. Production Rules • IF-THEN • Independent part, combined with other pieces, to produce better result • Model of human behavior • Examples • IF condition, THEN conclusion • Conclusion, IF condition • If condition, THEN conclusion1 (OR) ELSE conclusion2

  33. Knowledge Representation 5:Logic • Prepositional and Predicate logic are formal systems for exact reasoning(talking in details later) • Logic can also be used to represent: • Set properties • Properties through time • Semantic nets and OAV triples • Frames • Definite clause grammars (DCGs)

  34. Logic Representation Logic Representation : Prepositional Logic and Predicate calculus Operator and Symbol AND , &, n OR V, +, NOT  ,~ IMPLIES ,  EQUIVALENCE  Computer Science FMIPA IPB Yeni Herdiyeni 2008 35

  35. Prepositional Logic Prepositional Logic represent and reason with proposition statement that are either true or false Ex : IF The car will not start  A AND It is too far to walk to work  B THEN I will miss work today  C A ^ B  C Computer Science FMIPA IPB Yeni Herdiyeni 2008 36

  36. Predicate Logic A = John likes marry Likes (John, Marry)  Predicate Logic Symbol predicate logic : Constant : John, Mary, temperature Predicate : Likes variable : Likes (X, Y) Function : father(jack) = bob mother(judy) = kathy Operation: likes (X, Y) ^ likes (Z,Y)  ~Likes (X,Z) x Likes (X, mary) Computer Science FMIPA IPB Yeni Herdiyeni 2008 37

  37. Pustaka • Dari berbagai sumber

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