INC 551 Artificial Intelligence Lecture 2. Review Environment Action Sense, Perceive Make Decision Agent World Model Deliberative Agent.

งานนำเสนอเรื่อง: "INC 551 Artificial Intelligence Lecture 2. Review Environment Action Sense, Perceive Make Decision Agent World Model Deliberative Agent."— ใบสำเนางานนำเสนอ:

1 INC 551 Artificial Intelligence Lecture 2

2 Review Environment Action Sense, Perceive Make Decision Agent World Model Deliberative Agent

3 Problem Solving Agents Agents with world model

4 State-Action Model (Finite State Machine) State1State2State3 Action อยู่ในรูปของ state - action

5 Search for Solutions Tree Structure node Parent Child

6 ขั้นตอนในการทำ Problem Solving 1. Define Environment 2. Build/Implement Agent with search techniques

7 Define Environment • Define Problems and solutions Initial state, goal, path cost • Define measuring performance • Choose states and actions

8 Example

9 States: Location of each 8 tiles Initial state: as in picture Action: blank moves left, right, up,down Goal state: all tiles match the position Path cost: 1

10 Example Vacuum problem

11 States: Agent in 1 of 2 locations, Dirt can be anywhere, total of 8 possible states Initial state: Any Action: left, right, suck Goal state: all space are clean Path cost: each step cost 1

12 Example

13

14 Search Techniques Uninformed search • Breadth first • Uniform cost search • Depth first • Depth-limit search • Bidirectional search Informed search • Greedy best-first • A* search

15

16

17

18

19 Problems • Memory • Time เช่นที่ 12 depth, branching factor = 10, ใช้ 35 ปี 10000 tetrabytes

20

21 Start dpe

22

23 dpe ecb

24 de ecbrh p

25 dpe ecbrh q

26

27 dpe ecbrh q a

28 dpe ecbrh q aa

29 dpe ecbrh q aa r

30 dpe ecbrh q aa rq

31 dpe ecbrh q aa rqf Depth 1 Depth 0 Depth 2 Depth 3

32

33

34

35

36

37

38

39

40

41 Search Technique Properties • Completeness – guarantee ว่าจะเจอ solution ถ้า exist • Time Complexity – เวลาที่ใช้ในการ search • Space Complexity – memory ที่ใช้ในการ search • Optimality – solution ที่ได้ guarantee ว่าดีที่สุด

42 Characteristic of Breadth-first search Complete – yes ถ้า branching factor มีค่าจำกัด Search Time - มาก Space - มาก Optimal – no in general (optimal ถ้า cost =1 ทุก step)

43

44 State = Volume of water in each bottle (0,0) Action = How many ways to make state change? Transition 1.Empty Bottle 1 2.Empty Bottle 2 3.Fill in Bottle 1 4.Fill in Bottle 2 5.Pour Bottle 1 to 2 6.Pour Bottle 2 to 1

45 (0,0) Empty Bottle 1Empty Bottle 2 Fill in Bottle 1 Fill in Bottle 2Pour Bottle 1 to 2Pour Bottle 2 to 1 (3,0) (0,5)

46 (0,0) Empty Bottle 1Empty Bottle 2 Fill in Bottle 1 Fill in Bottle 2Pour Bottle 1 to 2Pour Bottle 2 to 1 (3,0) (0,5) (3,5)(0,3)(3,2)

47 (0,0) Empty Bottle 1Empty Bottle 2 Fill in Bottle 1 Fill in Bottle 2Pour Bottle 1 to 2Pour Bottle 2 to 1 (3,0) (0,5) (3,5)(0,3)(3,2) (3,3)(0,2)

48 (0,0) Empty Bottle 1Empty Bottle 2 Fill in Bottle 1 Fill in Bottle 2Pour Bottle 1 to 2Pour Bottle 2 to 1 (3,0) (0,5) (3,5)(0,3)(3,2) (3,3) (1,5) (0,2) (2,0)

49 (0,0) Empty Bottle 1Empty Bottle 2 Fill in Bottle 1 Fill in Bottle 2Pour Bottle 1 to 2Pour Bottle 2 to 1 (3,0) (0,5) (3,5)(0,3)(3,2) (3,3) (1,5) (0,2) (2,0) (1,0)(2,3)(2,5)

50 (0,0) Empty Bottle 1Empty Bottle 2 Fill in Bottle 1 Fill in Bottle 2Pour Bottle 1 to 2Pour Bottle 2 to 1 (3,0) (0,5) (3,5)(0,3)(3,2) (3,3) (1,5) (0,2) (2,0) (1,0)(2,3)(2,5) (3,4) Depth 1 Depth 0 Depth 2 Depth 3 Depth 4 Depth 5 Depth 6

51 Experiment Water Jug puzzle 19,13,7 Each student bring a paper

52 (0,13,7) Actions Pour 1 to 2 Pour 1 to 3 Pour 2 to 1 Pour 2 to 3 Pour 3 to 1 Pour 3 to 2 How many minimum move required to solve the puzzle?

53 How to write a computer program to do this for you?

54 Memory - > Paper to write something down Node structure State + Action + Back pointer Build tree until the goal is met

55 0-13-7,x,x

56 13-0-7,3,17-13-0,5,1

57 Search technique Uninformed search • Breadth first • Uniform cost search • Depth first • Depth-limit search • Bidirectional search Informed search • Greedy best-first • A* search ต่างกันตรงที่ จะ expand node ไหนต่อไป

58 Uniform-cost search จะ expand node ที่มี cost รวมมาจากจุดเริ่มต้น (accumulative cost) น้อยที่สุด

59 Example

60

61

62

63

64 Characteristic of Uniform-cost search Note: ถ้าทุก step มี cost เท่ากันหมด Uniform-cost search จะเหมือนกับ Breadth-first search Complete – yes ถ้า step cost ทุกอัน > 0 Search Time – มาก ( ขึ้นกับปัญหา ) # node ที่ cost < optimal solution Space – มาก ( ขึ้นกับปัญหา ) # node ที่ cost < optimal solution Optimal – yes

65 Search technique Uninformed search • Breadth first • Uniform cost search • Depth first • Depth-limit search • Bidirectional search Informed search • Greedy best-first • A* search

66 Depth-first Search จะ expand node ที่ลึกที่สุด

67

68

69

70

71 Characteristic of Depth-first search Complete – no Search Time – มาก Space – น้อย (linear space) Optimal – no