1. CPS 100, Spring 2009 8.1 Search, Trees, Games, Backtracking l Trees help with search  Set, map: binary search tree, balanced and otherwise  Quadtree and more, scenes and objects in games  3-4 Trees, KD-trees, search for files or points in “space”  Gametrees, search for game-playing l Intelligent search  How do we find the “best move” in a game?  How do we find optimal needle in haystack?  When is brute-force, exhaustive search (im)possible?

2. CPS 100, Spring 2009 8.2 Search, Backtracking,Heuristics l How do you find a needle in a haystack?  How does a computer play chess?  Why would you write that program?  l How does Mapquest/Googlemap find routes from one place to another?  Shortest path algorithms  Longest path algorithms l Optimal algorithms and heuristic algorithms  When is close good enough? How do measure “closeness”?  When is optimality important, how much does it cost?

3. CPS 100, Spring 2009 8.3 Exhaustive Search/Heuristics l We use binary search trees to organize data, in searching we don’t need to examine all the data to find what we’re looking for  Where is the smallest item in a search tree? Largest?  How are trees balanced? l What do we do when the search space is huge?  How many chess boards are there?  How many routes are there between my house and yours? l Exhaustive search: look at everything!

4. CPS 100, Spring 2009 8.4 Classic problem: N queens l Can queens be placed on a chess board so that no queens attack each other?  Easily place two queens  What about 8 queens? l Make the board NxN, this is the N queens problem  Place one queen/column  Horiz/Vert/Diag attacks l Backtracking  Tentative placement  Recurse, if ok done!  If fail, undo tentative, retry l wikipedia-n-queens wikipedia-n-queens

5. CPS 100, Spring 2009 8.5 Backtracking idea with N queens l Try to place a queen in each column in turn  Try first row in column C, if ok, move onto next column  If solved, great, otherwise try next row in column C, place queen, move onto the next column Must unplace the placed queen to keep going l What happens when we start in a column, where to start?  If we fail, move back to previous column (which remembers where it is/failed)  When starting in a column anew, start at beginning When backing up, try next location, not beginning l Backtracking in general, record an attempt go forward  If going forward fails, undo the record and backup

6. CPS 100, Spring 2009 8.6 N queens backtracking: Queens.java public boolean solve(int col){ if (col == mySize) return true; // try each row until all are tried for(int r=0; r < mySize; r++){ if (myBoard.safeToPlace(r,col)){ myBoard.setQueen(r,col,true); if (solve(col+1)){ return true; } myBoard.setQueen(r,col,false); } return false; }

7. CPS 100, Spring 2009 8.7 Basic ideas in backtracking search l We need to be able to enumerate all possible choices/moves  We try these choices in order, committing to a choice  If the choice doesn’t pan out we must undo the choice This is the backtracking step, choices must be undoable l Process is inherently recursive, so we need to know when the search finishes  When all columns tried in N queens  When we have found the exit in a maze  When every possible moved tried in Tic-tac-toe or chess? Is there a difference between these games? l Summary: enumerate choices, try a choice, undo a choice, this is brute force search: try everything

8. CPS 100, Spring 2009 8.8 Pruning vs. Exhaustive Search l If we consider every possible placement of 4 queens on a 4x4 board, how many are there? (N queens)  4x4x4x4 if we don’t pay attention to any attacks  4x3x2x1 if we avoid attacks in same row l What about if we avoid diagonal attacks?  Pruning search space makes more search possible, still could be lots of searching to do! l Estimate how long to calculate # solutions to the N- queens problem with our Java code….

9. CPS 100, Spring 2009 8.9 Queens Details l How do we know when it’s safe to place a queen?  No queen in same row, or diagonal  For each column, store the row that a queen is in  See QBoard.java for details l For GUI version, we use a decorator  The QBoardGUI is an IQueenState class and it has an IQueenState object in it  Appears as an IQueenState to client, but uses an existing one to help do its work  One of many object oriented design patterns, seen in Huff in the BitInputStream class

10. CPS 100, Spring 2009 8.10 Daphne Koller l 2004, Macarthur l 2008, first ACM/Infosys l http://tinyurl.com/3tdlug http://tinyurl.com/3tdlug “The world is noisy and messy …You need to deal with the noise and uncertainty.” “I find it distressing that the view of the field is that you sit in your office by yourself surrounded by old pizza boxes and cans of Coke, hacking away at the bowels of the Windows operating system,” she said. “I spend most of my time thinking about things like how does a cell work or how do we understand images in the world around us?”

11. CPS 100, Spring 2009 8.11 Computer v. Human in Games l Computers can explore a large search space of moves quickly  How many moves possible in chess, for example? l Computers cannot explore every move (why) so must use heuristics  Rules of thumb about position, strategy, board evaluation  Try a move, undo it and try another, track the best move l What do humans do well in these games? What about computers?  What about at Duke?

12. CPS 100, Spring 2009 8.12 Games at Duke l Alan Biermann  Natural language processing  Compsci 1: Great Ideas  Duchess, checkers, chess l Tom Truscott  Duke undergraduate working with/for Biermann  Usenet: online community l Second EFF Pioneer Award (with Vint Cerf!) Second EFF Pioneer Award (with Vint Cerf!)

13. CPS 100, Spring 2009 8.13 Heuristics l A heuristic is a rule of thumb, doesn’t always work, isn’t guaranteed to work, but useful in many/most cases  Search problems that are “big” often can be approximated or solved with the right heuristics l What heuristic is good for Sudoku?  Is there always a no-reasoning move, e.g., 5 goes here?  What about “if I put a 5 here, then…”  Do something else? http://en.wikipedia.org/wiki/Algorithmics_of_sudoku http://en.wikipedia.org/wiki/Algorithmics_of_sudoku l What other optimizations/improvements can we make?  For chess, checkers: good heuristics, good data structures

14. CPS 100, Spring 2009 8.14 Boggle Program

15. CPS 100, Spring 2009 8.15 Boggle Search for Word l Starting at board location (row,col) to find a string s  We want to keep track of where we are in the string  We want to keep track of what board locations we’ve used l How do we know when we’re done?  Base case of recursive, backtracking call  Where we are in the string? l How do we keep track of used locations?  Store in array list: tentatively use current one, recurse  If we don’t succeed, take off the last one stored!

16. CPS 100, Spring 2009 8.16 Backtracking, minimax, game search l We’ll use tic-tac-toe to illustrate the idea, but it’s a silly game to show the power of the method  What games might be better? Problems? l Minimax idea: two players, one maximizes score, the other minimizes score, search complete/partial game tree for best possible move  In tic-tac-toe we can search until the end-of-the game, but this isn’t possible in general, why not?  Use static board evaluation functions instead of searching all the way until the game ends l Minimax leads to alpha-beta search, then to other rules and heuristics

17. CPS 100, Spring 2009 8.17 Minimax, see TicTac.java l Players alternate, one might be computer, one human (or two computer players) l Simple rules: win scores +10, loss scores –10, tie is zero  X maximizes, O minimizes l Assume opponent plays smart  What happens otherwise? l As game tree is explored is there redundant search?  What can we do about this? X O X X O X X O X X X O X O X O X O X O X O X O X X O X O

18. CPS 100, Spring 2009 8.18 Interlude for trees l Joyce Kilmer Joyce Kilmer l Balanced Trees  Splay  Red-Black  AVL  B-tree

19. CPS 100, Spring 2009 8.19 Tree functions (repeat, review) l Compute height of a tree, what is complexity? int height(Tree root){ if (root == null) return 0; else { return 1 + Math.max(height(root.left), height(root.right) ); } l Modify function to compute number of nodes in a tree, does complexity change?  What about computing number of leaf nodes?

20. CPS 100, Spring 2009 8.20 Balanced Trees and Complexity l A tree is height-balanced if  Left and right subtrees are height-balanced  Left and right heights differ by at most one boolean isBalanced(Tree root){ if (root == null) return true; return isBalanced(root.left) && isBalanced(root.right) && Math.abs(height(root.left) – height(root.right)) <= 1; }

21. CPS 100, Spring 2009 8.21 Rotations and balanced trees l Height-balanced trees  For every node, left and right subtree heights differ by at most 1  After insertion/deletion need to rebalance  Every operation leaves tree in a balanced state: invariant property of tree l Find deepest node that’s unbalanced then make sure:  On path from root to inserted/deleted node  Rebalance at this unbalanced point only Are these trees height- balanced?

22. CPS 100, Spring 2009 8.22 What is complexity? l Assume trees are “balanced” in analyzing complexity  Roughly half the nodes in each subtree  Leads to easier analysis l How to develop recurrence relation?  What is T(n)?  What other work is done? l How to solve recurrence relation  Plug, expand, plug, expand, find pattern  A real proof requires induction to verify correctness

23. CPS 100, Spring 2009 8.23 Balanced trees we won't study l B-trees are used when data is both in memory and on disk  File systems, really large data sets  Rebalancing guarantees good performance both asymptotically and in practice. Differences between cache, memory, disk are important l Splay trees rebalance during insertion and during search, nodes accessed often more closer to root  Other nodes can move further from root, consequences? Performance for some nodes gets better, for others …  No guarantee running time for a single operation, but guaranteed good performance for a sequence of operations, this is good amortized cost (ArrayList.add)

24. CPS 100, Spring 2009 8.24 Balanced trees we will study l Both kinds have worst-case O(log n) time for tree operations l AVL (Adel’son-Velskii and Landis), 1962  Nodes are “height-balanced”, subtree heights differ by 1  Rebalancing requires per-node bookkeeping of height  http://webpages.ull.es/users/jriera/Docencia/AVL/AVL tree applet.htm http://webpages.ull.es/users/jriera/Docencia/AVL/AVL tree applet.htm l Red-black tree uses same rotations, but can rebalance in one pass, contrast to AVL tree  In AVL case, insert, calculate balance factors, rebalance  In Red-black tree can rebalance on the way down, code is more complex, but doable  Standard java.util.TreeMap/TreeSet use red-black

25. CPS 100, Spring 2009 8.25 Rotation doLeft (see AVLSet.java) l Why is this called doLeft?  N will no longer be root, new value in left.left subtree  Left child becomes new root l Unbalanced by two (not one!)  If left, left (or right, right) doLeft (doRight)  Otherwise need two doLeft/doRight  First to get to left, left Or to right, right A B C N A B C N Node doLeft(Node root) { Node newRoot = root.left; root.left = newRoot.right; newRoot.right = root; return newRoot; }

26. CPS 100, Spring 2009 8.26 Rotation to rebalance l Suppose we add a new node in right subtree of left child of root  Single rotation can’t fix  Need to rotate twice l First stage is shown at bottom  Rotate blue node right (its right child takes its place)  This is left child of unbalanced B A C N A B C N AB1B1 B2B2 C ????? Node doRight(Node root) { Node newRoot = root.right; root.right = newRoot.left; newRoot.left = root; return newRoot; } B2B2 A B1B1

27. CPS 100, Spring 2009 8.27 Double rotation complete AB1B1 B2B2 C B2B2 A B1B1 C B2B2 A B1B1 C l Calculate where to rotate and what case, do the rotations Node doRight(Node root) { Node newRoot = root.right; root.right = newRoot.left; newRoot.left = root; return newRoot; } Node doLeft(Node root) { Node newRoot = root.left; root.left = newRoot.right; newRoot.right = root; return newRoot; }

28. CPS 100, Spring 2009 8.28 AVL tree practice l Insert into AVL tree:  18 10 16 12 6 3 8 13 14  After adding 16: doLeftRight  After 3, doLeft on 16 18 10 18 16 18 10 16 doRight 18 16 10 doLeft 10 16 18 126 10 16 18 126 3 6 10 16 3 18 12 16 18 10 6 3 8 16 10 18

29. CPS 100, Spring 2009 8.29 AVL practice: continued, and finished l After adding 13, ok l After adding14, not ok  doRight at 12 12 16 18 10 6 3 812 16 18 10 6 3 8 13 14 13 16 18 10 6 3 8 12 14

30. CPS 100, Spring 2009 8.30 Dame Wendy Hall l Multimedia, “web before the web”, President of ACM, cofounder of WSRI (web science research institute) "....We are living in very unpredictable times....Every country, whether developed or the developing, need people with IT skills....What we need are people who can innovate. I think there is a unique opportunity to say these are the types of skills we need young people to have going forward...."