1. Intelligent Systems (2II40) C2 Alexandra I. Cristea September 2005

2. Outline II.Intelligent agents III.Search

3. II. Intelligent agents 1.Rational agent 2.Agent & its environment 3.Example: a simple agent 4.Rationality? 5.Task environment: A.PEAS B.Properties of the task environment 6.Agent properties

4. The agent and its environment E.g.: humans, robots, softbots but also thermostats

5. What kind of problem do these funny stories point at?

7. Rational Agent: Generalities We seek agent with best performance for given capabilities: –function from percept history to action: f: P* -> A –environment(s) + task(s) –computational limitations: given machine resources

11. Rationality (intelligence?)

13. Rationality – complete def. For each possible percept sequence & task in a given environment, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence, its resources, its performance measure and whatever built- in knowledge the agent has.For each possible percept sequence & task in a given environment, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence, its resources, its performance measure and whatever built- in knowledge the agent has.

14. Test: Is the agent rational? A.- environment + what is known about the environment? B.- task + what is known about the given task? C.- machine resources + what is known about the given machine resources? D.- percept sequence + what is known about the precept sequence up to date? E.- agent actions F.- is there a performance measure? G.- After the questions above are answered, we have to check if the performance measure is maximized

15. What kind of problem do these funny stories point at?

17. PEAS II.5.A. Specifying the task environment: PEAS

18. The agent and its environment

21. (Optional homework)

22. II.5.B. Properties of task environment

30. II.6. Agent types

37. Conclusion agents Agent is ‘something’ that perceives & acts in an environment. –Extra exercise: find alternative definitions! A Rational Agent acts so that it maximizes the performance measure. A task environment includes: performance measure, external env., actuators, sensors. Basic agent program design: reflex, model/ goal/ utility –based, learning agents

38. Homework 2 – part I 1.Develop a PEAS description for an automatic bus (e.g., Phileas) a chess playing robot (e.g, Deep Blue) and your own agent; give the proprieties of the task environment for each; select a suitable agent design. 2.Define a rational agent in a limited, closed world, as you will be using for your project, and show that it is rational (use the complete definition of rationality defined in C2 and the rationality test).

39. Outline II.Intelligent agents III.Search 1.Uninformed 2.Informed A.Heuristic B.Local C.Online 3.Constraints satisfaction

40. Search applications Obvious: –Finding Olympic Games schedule on the Web. –Finding the cheapest trip between here and Tokyo. –A robot navigating an environment strewn with obstacles. –A web-crawler indexing web pages Less Obvious: –Playing Chess –Job Shop Scheduling –Planning a party

41. Search ingredients nodesnodes : locations arcsarcs : connections between nodes –directed –directed : only be traversed in one direction netgraphnet or graph : collection of arcs & nodes –tree –tree : if  node has unique parent (w one exception) –root –root : exception; has no parents

42. A Directed Graph (DG) –In fact a Directed Acyclic Graph (DAG) L S O P Q F M N

43. Search –If we want to search through this graph from S to F this graph can be viewed as a tree.

44. Search algorithms

45. General search Offline, simulated exploration of state- space Generating successors of already explored states (expanding)

46. Example: traveling in Romania

47. General search example Arad

48. General search example Arad ZerindSibiuTimisoara

49. General search example Arad ZerindSibiuTimisoara AradOradeaFagarash Ramnicu Valcea SibiuBucharest

50. Implementation of general search

51. States vs. nodes Fagarash SibiuBucharest Depth=2 parent children In:Fagarash State Node

52. Strategy characteristics order node expansion =? parameters: –Completeness : solution? –Optimality : best solution? –Complexity: Time : max no. steps to solution Space : nodes in memo parameters of complexity computation: –b –b : max branching factor of search tree –d –d : depth of least-cost solution –m –m : max depth of state space

53. III.1.Uninformed search algorithms

54. Breadth-first search Expand shallowest node first Implementation: FIFO queue

55. Breadth-first example Arad

56. Breadth-first example Arad ZerindSibiuTimisoara

57. AradOradeaArad Breadth-first example Arad ZerindSibiuTimisoara OradeaFagarash Ramnicu Valcea AradLugoj

58. AradOradeaArad Breadth-first example Arad ZerindSibiuTimisoara OradeaFagarash Ramnicu Valcea Sibiu Bucharest AradLugoj Zerind Sibiu Timisoara Zerind Sibiu Zerind Sibiu Timisoara Zerind Sibiu Craiova Pitesti Zerind Sibiu Timisoara Mehadia

59. Proprieties of breadth-first search Complete?Complete? Time?Time? Space?Space? Optimal?Optimal?

60. Proprieties of breadth-first search Complete?Complete? Yes (if: b, d finite) Time?Time? O(b d+1 ) Space?Space? O(b d+1 ) Optimal?Optimal? Yes (if: b, d finite & cost/step=1) Problem: space!!

61. Depth-first search Expand deepest node first Implementation: LIFO queue

62. Breadth-first v’s Depth First Breadth-first Depth-first

63. Depth-first example Arad

64. Depth-first example Arad ZerindSibiuTimisoara

65. AradOradea Depth-first example Arad ZerindSibiuTimisoara

66. AradOradea Depth-first example Arad ZerindSibiuTimisoara Zerind Sibiu Timisoara …

67. Iterative deepening search Depth first search with growing depth l l = allowed maximal depth in tree

68. Iterative deepening search example Arad l = 0

69. Iterative deepening search example Arad l = 1

70. Iterative deepening search example l = 1 Arad ZerindSibiuTimisoara

71. Iterative deepening search example Arad l = 2

72. Iterative deepening search example l = 2 Arad ZerindSibiuTimisoara

73. Iterative deepening search example l = 2 AradOradea Arad ZerindSibiuTimisoara

74. Iterative deepening search example l = 2 Arad SibiuTimisoara OradeaFagarash Ramnicu Valcea

75. Iterative deepening search example l = 2 Arad Timisoara AradLugoj

76. Proprieties of iterative deepening search Complete?Complete? Yes (b,d finite) Time?Time? (d+1) + db + (d-1)b 2 + …+ b d = O(b d ) Space?Space? O(bd) Optimal?Optimal? Yes (b,d finite & cost/step=1)

77. Homework 2 – part II 3.Compute the proprieties of the depth-first search (completeness, time -, space complexity, optimality). Hint: some of the memory can be freed after usage.

78. Other info Check IS course 2II40 homepage for info on: –Evaluation of homeworks –Projects & grouping & deadlines http://wwwis.win.tue.nl/~acristea/HTML/IS/http://wwwis.win.tue.nl/~acristea/HTML/IS/http://wwwis.win.tue.nl/~acristea/HTML/IS/