1. 1 CSE 4705 Artificial Intelligence Jinbo Bi Department of Computer Science & Engineering http://www.engr.uconn.edu/~jinbo

2. 2 TodayToday Intelligent Agents

3. 3 Inverted pendulum Example to demonstrate a learning agent

4. 4 8-puzzle8-puzzle A tile adjacent to the blank space can slide into the space.

5. 5 Holiday in Romania Start Goal

6. 6 Complexity of Breadth-First Search

7. 7 Holiday in Romania Start Goal

8. 8 ComparisonComparison

9. 9 Demonstration on Games/Robots Breadth First Search Pink: starting point Blue: goal Teal: scanned squares Darker: closer to starting point

10. 10 Demonstration on Games/Robots An optimal informed search algorithm A* We add a heuristic estimate of distance to the goal Yellow: examined nodes with high h(n) Blue: examined nodes with low h(n)

11. 11 Demonstration on Games/Robots Breadth-first search expands many many nodes Pink: starting node Dark blue: goal

12. 12 Demonstration on Games/Robots A* search expands much fewer nodes Pink: starting node Dark blue: goal

13. 13 Start Goal The distance from each city to Bucharest:

14. 14 Best-first Search

15. 15 Best-first Search

16. 16 A* Search

17. 17 A* Search

18. 18 A* Search

19. 19 Hill Climbing

20. 20 8-puzzle8-puzzle Start Goal

21. 21 Hill-Climbing Ex: 8-queens

22. 22 Gradient ascent/descent

23. 23 Gradient methods / Newton’s methods Contour lines of a function (Green: gradient descent, Red: Newton’s methods)

24. 24 Difficult Problems

25. 25 Difficult Problems

26. 26 Random Restart

27. 27 Genetic Algorithm https://www.youtube.com/watch?v=ejxfTy4lI6I A short video explains Genetic Algorithm in 3 minutes

28. 28 Genetic Algorithm

29. 29 Searching nondeterministic The 8 physical states of the vacuum world

30. 30 Searching nondeterministic Fig. 4.10, AND-OR Search Tree, and a depth-first search

31. 31 Searching nondeterministic Fig. 4.11, AND-OR Search algorithm (graph search) and a depth-first search, it returns a conditional plan that reaches a goal state in all circumstances S i in

32. 32 Searching partial observable Deterministic Non-deterministic Fig. 4.13

33. 33 Searching partial observable

34. 34 Searching partial observable A vacuum has local sensors, and can report a state of [location, dirty/clean]

35. 35 Searching partial observable Partial observations can still be quite useful (Fig. 4.18

36. 36 Game Tree for Tic-Tac-Toe

37. 37 An Evaluation Function for Tic-Tac-Toe f(n) = 8-8=0 f(n) = 8-5=3 f(n) = 8-6=2 f(n) = 2f(n) = 3 f(n): the potential # of lines with 3 x – the potential # of lines with three o f(n) = 0 if a tie f(n) = + ∞ if n is a terminal win f(n) = - ∞ if n is a terminal loss

38. 38 Two Players MINIMAX value for a Two-Play Game Tree

39. 39 Multiple Players

40. 40 Alpha-Beta Pruning

41. 41 Map Coloring

42. 42 A Consistent and Complete Solution to Map Coloring

43. 43 BacktrackingBacktracking

44. 44 Backtracking – Map Coloring

45. 45 Improving Backtracking Most constrained variables Most constraining variables

46. 46 Improving Backtracking Given n variables, choose the least constraining value

47. 47 Improving Backtracking Forward checking

48. 48 Arc Consistency: General Case If X-> Y is consistent iff for every value x of X there is some allowed y in Y that can be used

49. 49 Arc Consistency

50. 50 ≠ General Backtracking

51. 51 The Wumpus World http://www.flashrolls.com/puzzle-games/Hunt-The-Wumpus- Flash-Game.htm

52. 52 The Wumpus World Figure 7.3 The first step by the agent in the wumpus world

53. 53 The Wumpus World Figure 7.4 The second and third steps by the agent in the wumpus world

54. 54 The Wumpus World

55. 55 The Wumpus World Figure 7.5 Dotted line shows the model of (no pit in [1,2])

56. 56 The Wumpus World Figure 7.5 Dotted line shows the model of (no pit in [2,2])

57. 57 Truth Table

58. 58 Logical Equivalence

59. 59 Resolution Algorithm To prove KB entails α, we need to show KB -> α is valid. By proof of contradiction, we need to prove KB ˄ ~ α is unsatisfiable, which means the resolution algorithm will give clauses including an empty clause.

60. 60 Resolution Algorithm The Wumpus example

61. 61 Forward Chaining

62. 62 Forward Chaining (ex)

63. 63 Forward Chaining (ex)

64. 64 The WalkSAT Algorithm

65. 65 Hard Satisfiability Problems

66. 66 Hard Satisfiability Problems Median runtime for 100 satisfiable random 3-CNF statements, n=50