This document summarizes various informed search algorithms including greedy best-first search, A* search, and memory-bounded heuristic search algorithms like recursive best-first search and simple memory-bounded A* search. It discusses how heuristics can be used to guide the search towards optimal solutions more efficiently. Admissible and consistent heuristics are defined and their role in guaranteeing optimality of A* search is explained. Methods for developing effective heuristic functions are also presented.

1. Chapter 4 Informed Searching Dr. Mustafa Jarrar University of Birzeit [email_address] www.jarrar.info Lecture Notes, Advanced Artificial Intelligence (SCOM7341) University of Birzeit 2 nd Semester, 2011 Advanced Artificial Intelligence (SCOM7341)

2. Discussion and Motivation How to determine the minimum number of coins to give while making change?  The coin of the highest value first ?

5. Romania with step costs in km Suppose we can have this info (SLD)  How can we use it to improve our search?

7. Greedy best-first search example

8. Greedy best-first search example

9. Greedy best-first search example

10. Greedy best-first search example

14. A * search example

15. A * search example

16. A * search example

17. A * search example

18. A * search example

19. A * search example

20. A* Exercise How will A* get from Iasi to Fagaras?

21. A* Exercise Node Coordinates SL Distance A (5,9) 8.0 B (3,8) 7.3 C (8,8) 7.6 D (5,7) 6.0 E (7,6) 5.4 F (4,5) 4.1 G (6,5) 4.1 H (3,3) 2.8 I (5,3) 2.0 J (7,2) 2.2 K (5,1) 0.0

22. Solution to A* Exercise

23. Greedy Best-First Exercise Node Coordinates Distance A (5,9) 8.0 B (3,8) 7.3 C (8,8) 7.6 D (5,7) 6.0 E (7,6) 5.4 F (4,5) 4.1 G (6,5) 4.1 H (3,3) 2.8 I (5,3) 2.0 J (7,2) 2.2 K (5,1) 0.0

24. Solution to Greedy Best-First Exercise

25. Another Exercise Node C g(n) h(n) A (5,10) 0.0 8.0 B (3,8) 2.8 6.3 C (7,8) 2.8 6.3 D (2,6) 5.0 5.0 E (5,6) 5.6 4.0 F (6,7) 4.2 5.1 G (8,6) 5.0 5.0 H (1,4) 7.2 4.5 I (3,4) 7.2 2.8 J (7,3) 8.1 2.2 K (8,4) 7.0 3.6 L (5,2) 9.6 0.0 Do 1) A* Search and 2) Greedy Best-Fit Search

35. Recursive Best First Search (RBFS) RBFS changes its mind very often in practice. This is because the f=g+h become more accurate (less optimistic) as we approach the goal. Hence, higher level nodes have smaller f-values and will be explored first. Problem? If we have more memory we cannot make use of it. Ay idea to improve this? best alternative over fringe nodes, which are not children: do I want to back up?

37. SMA* pseudocode function SMA*( problem ) returns a solution sequence inputs : problem , a problem static : Queue , a queue of nodes ordered by f -cost Queue  MAKE-QUEUE({MAKE-NODE(INITIAL-STATE[ problem ])}) loop do if Queue is empty then return failure n  deepest least-f-cost node in Queue if GOAL-TEST( n ) then return success s  NEXT-SUCCESSOR( n ) if s is not a goal and is at maximum depth then f( s )   else f( s )  MAX(f( n ),g( s )+h( s )) if all of n ’s successors have been generated then update n ’s f -cost and those of its ancestors if necessary if SUCCESSORS( n ) all in memory then remove n from Queue if memory is full then delete shallowest, highest-f-cost node in Queue remove it from its parent’s successor list insert its parent on Queue if necessary insert s in Queue end

38. Simple Memory-bounded A* (MA*) 24+0=24 A B 12 15 H 13  A G 18 13[15]  f = g+h (Example with 3-node memory) Progress of MA*. Each node is labeled with its current f -cost. Values in parentheses show the value of the best forgotten descendant. Can tell you when best solution found within memory constraint is optimal or not.  = goal Search space A B G C D E F H J I K 0+12=12 10+5=15 20+5=25 30+5=35 20+0=20 30+0=30 8+5=13 16+2=18 24+0=24 24+5=29 10 8 10 10 10 10 8 16 8 8 A 12 A B G 13 15 13 A G 24[  ] I 15[15] 24 A B G 15 15 24 A B C 15[24] 15 25 A B D 8 20 20[24] 20[  ]

51. Hill-climbing search: 8-queens problem A local minimum with h = 1

57. Genetic Algorithms [2,6,9,3,5 0,4,1,7,8] [3,6,9,7,3 8,0,4,7,1] [2,6,9,3,5,8,0,4,7,1] [3,6,9,7,3,0,4,1,7,8]