Introduction to Innovative Design Thinking

1. Introduction to Innovative Design Thinking CDI

2. Lecture 4 • Concept of Fuzzy Logic • Lateral thinking • Six Thinking Hats • Problem Identification

3. Fuzzy Logic Fuzzy logic is a notion introduced by Lotfi Zadeh, a Russian professor in 1964.

4. Fuzzy Logic It is a notion of uncertainty. Unlike logical thinking in a dialectic deduction or induction pattern, fuzzy logic aims at investigating the Class – categories.

5. Fuzzy Logic Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth -- truth values between"completely true" and "completely false".

6. Fuzzy Logic The process of “fuzzification” as a methodology to generalize ANY specific theory from a crisp (discrete) to a continuous(fuzzy) form. Thus recently researchers have also introduced "fuzzy calculus", "fuzzy differential equations",and so on .

7. Fuzzy Logic Fuzzy logic depends on the degree of “truth”. The issue studying can be categorized into mathematical calculation and classify the in-between differences in the degree of “truth” and “fact”.

8. Fuzzy Logic New Perception Perception Concept Idea Heritage

9. Fuzzy Logic New Perception Proficiency of Languages Perception Concept Idea Heritage

10. Fuzzy Logic New Perception Superordinates Perception Ordinates Concept Subordinates Idea Heritage

11. Fuzzy Logic In classical set theory, a subset U of a set S can be defined as a mapping from the elements of S to the elements of the set {0,1}, U: S --> {0, 1}

12. Fuzzy Logic This mapping may be represented as a set of ordered pairs, with exactly one ordered pair present for each element of S. The first element of the ordered pair is an element of the set S, and the second element is an element of the set {0, 1}.

13. Fuzzy Logic The value zero is used to represent non-membership, and the value one is used to represent membership. The truth or falsity of the statement x is in U is determined by finding the ordered pair whose first element is x.

14. Fuzzy Logic The statement is true if the second element of the ordered pair is 1, and the statement is false if it is 0.

15. Fuzzy Logic Similarly, a fuzzy subset F of a set S can be defined as a set of ordered pairs, each with the first element from S, and the second element from the interval [0,1], with exactly one ordered pair present for each element of S

16. Fuzzy Logic This defines a mapping between elements of the set S and values in the interval [0,1]. The value zero is used to represent complete non-membership, the value one is used to represent complete membership, and values in between are used to represent intermediate DEGREES OF MEMBERSHIP.

17. Fuzzy Logic The set S is referred to as the UNIVERSE OF DISCOURSE for the fuzzy subset F. Frequently, the mapping is described as a function, the MEMBERSHIP FUNCTION of F. The degree to which the statement x is in F is true is determined by finding the ordered pair whose first element is x.

18. Fuzzy Logic The DEGREE OF TRUTH of the statement is the second element of the ordered pair. In practice, the terms "membership function" and fuzzy subset get used interchangeably.

19. Fuzzy Logic Let's talk about people and "tallness". In this case the set S (the universe of discourse) is the set of people. Let's define a fuzzy subset TALL, which will answer the question "to what degree is person x tall?"

20. Fuzzy Logic TALL as a LINGUISTIC VARIABLE, which represents our cognitive category of "tallness". To each person in the universe of discourse, we have to assign a degree of membership in the fuzzy subset TALL.

21. Fuzzy Logic The easiest way to do this is with a membership function based on the person's height. Tall(x) = { 0, if height(x) < 5 ft., (height(x)-5ft.)/2ft., if 5 ft. <= height (x) <= 7 ft., 1, if height(x) > 7 ft. }

22. Fuzzy Logic We can draw a graph like this: 1.0 0.5 0.0 5.0 7.0

23. Fuzzy Logic Given this definition, here are some example values:  Person Height degree of tallness Billy 3' 2" 0.00 [I think] Yoke 5' 5" 0.21 Drew 5' 9" 0.38 Erik 5' 10" 0.42 Mark 6' 1" 0.54 Kareem 7' 2" 1.00 [depends on who you ask]

24. Fuzzy Logic Expressions like "A is X" can be interpreted as degrees of truth, e.g., "Drew is TALL" = 0.38.

25. Fuzzy Logic The standard definitions in fuzzy logic are:  truth (not x) = 1.0 - truth (x) truth (x and y) = minimum (truth(x), truth(y)) truth (x or y) = maximum (truth(x), truth(y))

26. Fuzzy Logic This is a very commonly used mathematical calculation in developing artificial intelligence. The power of fuzzy logic depends on the ambiguity of the language.

27. Fuzzy Logic Hence, beyond profound calculation, we can make use of the concept to build up a fuzzy map, helping us to see the vague argument more clearly and thoroughly.

28. Lateral Thinking My true story: When I was studying design …… If you were me, what would you do in order to get back the pen???

29. Lateral Thinking As you can see, logical thinking sometimes does not help in problem solving. You have to find another way out.

30. Lateral Thinking Lateral thinking is a method introduced by Dr. Edward De Bono.

31. Lateral Thinking It is also known as Horizontal thinking. This method is totally different from the traditional logical thinking – Vertical thinking.

32. Lateral Thinking Problem Logical Thinking is a vertical thinking method started from the problem towards the solution in step by step approach. Solution

33. Lateral Thinking Unlike Logical thinking, lateral thinking encourage people to think all possible alternatives.

34. Lateral Thinking By lateral thinking, we are trying to propose as many “crazy” ideas as we can, without applying logic or knowledge.

35. Lateral Thinking If blue is the best proposal, we then started to build up the logic to study how the idea can be executed.

36. Lateral Thinking A If H? In lateral thinking, we only ask WHAT IF, and keep all nonsense as treasure. Do not, and never criticize in the lateral thinking process.

37. What if X A CX BX CDX EX Solution DEX Lateral Thinking U-shape thinking model Sometimes, we cannot depend on linear logical thinking. Using the U-shape model can help us keep on examining the problem.

38. Lateral Thinking A X X’ We can also set up the anti-design statement for the problem so as to create more ideas.

39. Lateral Thinking There are no fixed rules in lateral thinking. Hence, there are some points to note to arouse creativity.

40. Lateral Thinking • Encourage intuition. • Allows crazy ideas. • Simple is the best. • Make use of possibilities. • Treasure coincident.

41. Lateral Thinking An interesting question before you go: Why 7 + 6 equal to 10 ?

42. References • Lateral Thinking, Edward de Bono, 1985

43. Six Thinking Hats This is a thinking method introduced by Dr. Edward De Bono. It depends highly on role-playing technique.

44. Six Thinking Hats There are six different coloured thinking hats, which are White, Red, Black, Yellow, Green and Blue.

45. Six Thinking Hats PROBLEM

46. Six Thinking Hats White Hat: • Collecting Data and Facts • No interpretation and no personal opinion

47. Six Thinking Hats Red Hat: • Expression of one’s emotion and feeling. • No need to elaborate the reasons behind.

48. Six Thinking Hats Black Hat: • Collecting all negative comments. • It helps to build up the negative design criteria.

49. Six Thinking Hats Yellow Hat: • Optimistic opinions with reasons. • Constructive ideas with logical thinking

50. Six Thinking Hats Green Hat: • Creative ideas under lateral thinking. • Select the appropriate solution and skill.