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Lecture 7 knowledge Representation

Lecture 7 knowledge Representation. Lecturer/ Kawther Abas k.albasheir@sau.edu.sa. 363CS – Artificial Intelligence. What is Knowledge?. data – primitive verifiable facts, of any representation. Data reflects current world,often voluminous frequently changing.

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Lecture 7 knowledge Representation

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  1. Lecture 7 knowledge Representation Lecturer/ Kawther Abas k.albasheir@sau.edu.sa 363CS – Artificial Intelligence

  2. What is Knowledge? data– primitive verifiable facts, of any representation. Data reflects current world,often voluminous frequently changing. information– interpreted data knowledge– relation among sets of data (information), that is very often used for further information deduction. Knowledge is (unlike data) general. Knowledge contains information about behaviour of abstract models of the world.

  3. Data, Information, Knowledge ? Non-programmable Non-algorithmic(heuristic) WISDOM KNOWLEDGE INFORMATION Algorithmic programmable DATA

  4. Knowledge Representation Techniques Rules Semantic Networks Object-Attribute Value TECHNIQUES Logic Frames

  5. Object-Attribute-Value (OAV) • Using fact : “ • Eg: The ball’s color is red (assign red to the ball’s color) The object can be physical (eg: car, books) or abstract (eg: love, hobby). • The value can be numerical, string or Boolean! It could be either single or multi valued from different attributes and objects. Used in MYCIN

  6. OAV Triplets Diagram (i) Fact :=: “The chair’s color is red and priced at $ 35.00 ” RED Color CHAIR Priced $ 35.00 Object Attribute Value

  7. OAV Triplets Diagram (ii) Fact :=: “TIN 313 is a compulsory subject for MSc Int Sys., code for Artificial Intelligence, and taught by Mr Yousef Salahat” Compulsory subject MSc Int. Sys TIN 313 Mr Yousef Salahat Taught Code Artificial Intelligence

  8. Rules Based • IF condition THEN action statements.      (premise              (goal      antecedent)          consequent) • Example  IF “Temperature is hot” THEN “turn on the air-conditioning system”

  9. Ball’s color = red Like = ball Will buy = ball Rules Based System (I) Rule 1:IF the ball’s color is red THEN I like the ball. Rule 2:IF I like the ball THEN I will buy the ball. Knowledge Base 3 IF ball’s color = red THEN like = ball IF like = ball THEN will buy the ball Question: Ball’s color? Answer: Red Working Memory 1 2 5 4

  10. Patient has Strep throat Rules Based System (II) • Rule 1: IF x has a sore throat AND suspect bacterial infection THEN x has strep throat • Rule 2: IF x temperature is > 37 c THEN x has a fever • Rule 3: IF x has been sick > a month AND x has a fever THEN suspect bacterial infection • Patient’s temperature = 38 c حرارة المريض 38 • Patient has been sick > 2 months المريض تعبان من شهرين • Patient has a sore throat المريض لديه التهاب حلق • Conclusion ? المريض لديه بكتيريا في الحلق

  11. The Example of Semantic Networks (Bird) FACT : Parrot is a bird. Typically bird has wings and travel by flying. Bird category falls under animal kingdom. All animal requires air to breathe. Ostrich is a bird but travels by walk. Wings has is-a Parrot Bird Air is-a Animal Breathe Fly travel Ostrich Walk “exceptional handling” travel

  12. Frames Structure Frame Name: BIRD Frame Name: OSTRICH Properties: Class Name: BIRD Color = unknown Wings = 2 Flies = True Properties: Color = brown/dark Wings = 2 Flies = False

  13. Logic المنطق الرياضي • The oldest representation existed • Implemented using PROLOG, LISP programming language.

  14. Logical Operators

  15. Facts • Artificial intelligence is a computer system • Cat is an animal • Or combine • Ahmed mother is married to Khalid father = True • Cat is human = false

  16. Rules القواعد • Easy come easy go • every way has an answer • or • If • animal give milk it is a mammal

  17. Predicate Calculus Logic (FOPL) operator (variables_1, variables_2,…) EXAMPLES: COMPUTER_COURSE(ARTIFICIAL_INTELLEGIENCE) ANIMAL(CAT)

  18. Mathmatical Logic

  19. Predicate Calculus Logic (FOPL) • Example: “She likes chocolate”  likes (she, chocolate). • Universal quantifier (X) to show all object is true like(chocolate) • Existential quantifier (X) to show existence / partial object is true [ Eg: Some people ( X (people (X))]

  20. The Example of FOPL Normal: “If it doesn’t rain today, Ahmad will go to the beach. FOPL:rain( today) go(Ahmad, beach) Normal: “All volleyball players are tall” FOPL:X (volleyball_player (X)  tall (X)) Normal: Some people like durian. FOPL:X (person(X)  likes(X, durian)) Normal: Nobody likes wars FOPL: X likes (X, wars)

  21. Implementing Propositional Logic • “IF the battery is dead THEN the car won’t start” • P = battery is dead & Q = car won’t start • Battery is dead = T, car won’t start = T • “Battery not dead” = F, “car will start” = F • Equivalence to P  Q

  22. Example: Mammals Subset-of legs 2 Person HasMother Subset of Female Person Male Person Subset-of Member of Member of Sister of Mariam Ahmad legs 1

  23. Example • Sister_of(Mariam,Ahmed) • Legs(Ahmed)=1 • Member_of(Mariam,Female_Person) حالة استثنائية: أحمد له رجلا واحدة بينما لكل البشر رجلان

  24. Resolution Theorem. Resolution is sound. Thai is, all derived formulas are entailed by the given ones Theorem: Resolution is refutationally complete. That is, if a clause set is unsatisfiable, then Resolution will derive the empty clause eventually. If a clause set is unsatisfiable and closed under the application of resolution inference rule then it contains the empty clause. 35

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