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

49. 49 ≠ General Backtracking

50. 50 The Wumpus World http://www.flashrolls.com/puzzle-games/Hunt-The-Wumpus- Flash-Game.htm