1. George F Luger ARTIFICIAL INTELLIGENCE 5th edition Structures and Strategies for Complex Problem Solving Structures and Strategies For Space State Search Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 3.0Introduction 3.1 Graph Theory 3.2Strategies for Space State Search 3.3Using Space State to Represent Reasoning with the Predicate Calculus 3.4Epilogue and References 3.5Exercises 1 To be discussed later

2. Figure 3.1: The city of Königsberg. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 2

3. Figure 3.2:Graph of the Königsberg bridge system. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 3

4. Figure 3.3:A labeled directed graph. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 4

5. Figure 3.4:A rooted tree, exemplifying family relationships. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 5

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7. Fig 3.5(a) The finite state graph for a flip flop and (b) its transition matrix. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 7

8. Fig 3.6(a) The finite state graph and (b) the transition matrix for string recognition example 8

9. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 9

10. Fig 3.8 State space of the 8-puzzle generated by “move blank” operations 10

11. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 3.9 An instance of the travelling salesperson problem 11

12. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 3.10Search for the travelling salesperson problem. Each arc is marked with the total weight of all paths from the start node (A) to its endpoint. 12

13. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 3.11An instance of the travelling salesperson problem with the nearest neighbor path in bold. Note this path (A, E, D, B, C, A), at a cost of 550, is not the shortest path. The comparatively high cost of arc (C, A) defeated the heuristic. 13

14. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 3.12State space in which goal-directed search effectively prunes extraneous search paths. 14 Stop Here!!

15. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 3.13State space in which data-directed search prunes irrelevant data and their consequents and determines one of a number of possible goals. 15 Skip!!

16. Function backtrack algorithm Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 16

17. Luger Backtracking vs. Nilsson’s “True Backtracking” Nilsson’s algorithms forgets all paths (and the states that are on the path) that lead to failure. Luger memorizes all previously visited states. Storage cost Nilsson (O(log b (n)); Luger (O(n))—b being the branching factor; the number of operators applicable for a given state. Nilsson creates new states only when necessary; Luger creates new states beforehand, which is inefficient. Luger backtracks for states that have been previously visited; Nilsson only backtracks for states that already occur on the current path. Nilssons algorithm only memorizes the currently explored path; for each state on the path it memorizes the operators that are left to be applied Nilssons’ code is kind of interpretive and recursive; however, it is easy to replace the recursive version by an iterative version. Nilsson contains plug-in functions for incorporating domain specific knowledge into the algorithm; Luger does not—which is bizarre for AI book.

18. A trace of backtrack on the graph of figure 3.12 Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 17

19. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 3.14Backtracking search of a hypothetical state space space. 18

20. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Fig 3.15Graph for breadth - and depth - first search examples. 19

21. Function breadth_first search algorithm Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 20

22. A trace of breadth_first_search on the graph of Figure 3.13 Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 21