Chapter Two Introduction to Artificial Intelligence

1. Chapter Two Introduction to Artificial Intelligence KNOWLEDGE REPRESENTATION Dr. Abbas Fadhil M. A. AL-Juboori Computer Science Dept. – Kerbala University abbaszain2003@yahoo.com Abbas.aljuboori@uokerbala.edu.iq

2. Knowledge Knowledge, like love, is one of those words that everyone knows the meaning of, yet finds hard to define. Like love, knowledge has many meanings. Giarratano and Riley (1998)

3. Knowledge “Intelligence requires knowledge” Intelligence refers to the capacity to acquire and apply knowledge.

4. Definition • Knowledge is an understanding which is gained through • experience; • familiarity with the way to do something to perform a task; • an accumulation of facts, procedural rules or heuristics.

5. Definition • Knowledge can have many meanings. It is not just the body of facts and principles accumulated by human-kind or the act, or state of knowing, but also the familiarity with languages, concepts, procedures, rules, ideas, abstractions, places, customs, facts and associations as well as information (Patterson, 1990)

6. Facts KNOWLEDGE Heuristic Procedural Rules

7. Fact • Facts • A statement that relates a certain element of truth about a subject matter or a domain • Example: • milk is white • the sun rises in the East and sets in the West

8. Procedure • Procedural rules • A rule that describes a sequence of relations relative to the domain • Example: • If the gas gauge shows quarter-full or less, then look for a gasoline station

9. Heuristic • Heuristic • Is a rule of thumb based on years of experience. • Example: • If a person drives no more than 5 miles above the speed limit, then that person is not likely to be stopped for speeding.

10. Priori vs Posteriori • Priori • Knowledge which cannot be denied • Considered to be universally true • Logic statements, mathematical laws • e.g. “everybody will die”, “ice is cold” • Posteriori • Knowledge derived from the senses, which can be true or false. • The truth can be denied by sensory experience or on the basis of new knowledge

11. Tacit vs Explicit • Tacit • Unconscious knowledge that cannot be expressed by language – spontaneous actions without any significant amount of effort • Eyes blinking, breathing • Explicit • Documented knowledge

12. Categories of Knowledge Declarative (what is it) Meta Knowledge (about other) Procedural (how to do) Basic Types of Knowledge Heuristic (shortcut) Structural (mental model)

13. Procedural • Knowledge on the process of doing something • It provides direction on how to do something via rules, strategies, agendas as well as procedures • Declarative • A passive knowledge expressed as statements of facts about the world

14. Meta-knowledge • Knowledge about knowledge • Knowledge on knowing which knowledge to use to solve a problem • Always used by experts to enhance the efficiency of problem solving • Heuristic • Knowledge which is gained through experience and translated into instinct or intuition • Often displayed individual expertise

15. Some examples • Procedural knowledge • to boil an egg we must do… then… • Declarative knowledge • my room no. is 2103 • Meta-knowledge • if you want to know about heart attack, please read this book • Heuristic knowledge • the clouds looks dark and heavy, … heavy rain might fall… • Structural Knowledge • a cat has four legs

16. This is CT’s phone no.! Information Data + CT Knowledge Hierarchy

17. MetaKnowledge Knowledge Information Data Noise Hierarchy of Knowledge

18. Source of Knowledge Not documented Exp: experience DocumentedExp: printed & electronic media

19. Knowledge Representation • Knowledge representation is “a science of translating actual knowledge into a format that can be used by the computer”.

20. Knowledge Usage Knowledge Representation Knowledge Source

21. Why needs to represent knowledge?... “You are given a project to develop a system that can diagnose heart attack?” • How can you get information about heart attack? • How do you understand the knowledge? • Which knowledge to get into computer?

22. KR method/technique Object Knowledge Representation Methods Logic Rule

23. OAV – Object Attribute Value Color Gold Object Attribute Value

24. OAV • Using fact : “form of declarative knowledge”

25. OAV • Refer to particular properties value of object • 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.

26. Car Colour OAV Colour Gold Object Attribute Value Gold

27. OAV • Fact :=: “The chair’s color is red and priced at ID 35.000 ” RED Color CHAIR Price ID 35.000 Object Attribute Value

28. OAV • Fact :=: “I have a brother named Johnny. The 8 years-old brother likes to play tennis and football.” gender male johnny 8 years old age hobby tennis football

29. OAV • Discussion – Describe about Doraemon

30. Semantic Network • Semantic Network Animals

31. Semantic Network • Definition • method of knowledge representation using a graph made up of nodes and arcs” • Graphical view of problem’s important objects, properties and relationships. • Nodes represent objects & arcs represent the relationship. • Arcs are commonly labeled with terms “IS-A” or “HAS”

32. Semantic Network • 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 walking. Wings has Parrot Bird Air is-a Animal is-a breathe travel Fly Ostrich travel Walk

33. Semantic Network Walk Mammals travel-by is-a Human Two legs has Female is-a is-a Male Mariam is-a System Analyst Ahmad mother-of is-a travel-by has Degree BIT(Hons) Wheel chair

34. Frame • Frame • Definition :: ”a data structure for representing stereotypical knowledge of some concept or object”

35. Frame • An extension version of semantic network – called “schema” (proposed by Barlett, 1932). • Basic concept of object oriented programming (proposed by Minsky, 1975). • Class frame  general characteristics of some common objects (Eg: class frame bird refer to common properties of bird). • Instance frame  to describe unique characteristic from class frame (Eg: class “ostrich” from class frame “bird”)

36. Frame • Example Frame Name: BIRD Properties: Color = <unknown> Wings = 2 Flies = True Frame Name: OSTRICH Class Name: BIRD Properties: Color = brown/dark Wings = 2 Flies = False

37. Frame Two elements of frame • Slot • Is the characteristic that describe an object • Exp: color, food, no. of wings, … • Facet • Value for slot • Exp: yellow, 1, worm,…

38. Frame • Example Frame Name: BIRD Properties: Color = <unknown> Wings = 2 Flies = True Slot Facet

39. Rules • Rule • Definition :: Rules ”a knowledge structure that relates some known information to other information and that can be concluded or inferred to be known”

40. Rules • Is a form of procedural knowledge  associates given information to some action. • Structure  connects antecedents (premises) and consequents (conclusions).

41. Rules • Statement “IF”  antecedent and “THEN”  consequent IF <antecedent> THEN <consequent> IF thirsty THEN drink_a_water

42. Rules • Example: • Diagnosing strep throat (knowledge base) Rule 1: IF x has a sore throat AND suspect bacterial infection THEN patient 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

43. Logic-based Representation • Logic

44. Logic-based Representation • Oldest form of KR in computer • Concerned with the truthfulness of a chain of statements • 2 kinds of logic: • Propositional Logic • Predicate Calculus • Implemented in PROLOG (Programming in Logic) language

45. Propositional Logic • E.g. it_is_raining kitty_is_outside kitty_gets_wet • Elementary propositions or atomic sentences – cannot be broken down into smaller meaningful units. • Often represented using symbols, e.g. P, Q, A etc.

46. Propositional Logic • Manipulate basic Boolean logic operations (AND, OR, NOT, IMPLIES, EQUIVALENCE.) • E.g.: • Normal :“Today is raining, therefore I will miss the class” • Logic :today_raining  i_will_miss_class • Combining two or more PL forms compound propositions (CP) or formulae. • CP consists of propositions and logical operators.

47. Propositional Logic • Logical operators:

48. Propositional Logic • Propositional Logic – Example • Example 1: • Normal: The sky is blue and windy. It is really great for picnic • Logic: sky_blue windy  great_for_picnic • Example 2: • Normal: If the weather is cloudy, then it will be raining. If it is raining, people will stay at home. • Logic: (weather_cloudy  raining)  (raining  people_stay_home). • Example 3: • Normal: I will rather stay if and only if it is raining. • Logic: i_will_stay  raining

49. Propositional Logic • Propositional Logic – Discussion Question: Transform each of the following statements into propositional logic: • Today is Sunday and it is a very lovely day. • It rained yesterday, therefore I've missed my lecture. • All men are mortals. • Fraihais a district within Kerbala.

50. Propositional Logic • Nested formulae – important to express the actual meaning. • E.g: it_is_raining kitty_is_outside  kitty_gets_wet (1) (it_is_raining kitty_is_outside)  kitty_gets_wet (2) it_is_raining (kitty_is_outside  kitty_gets_wet)