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Game Playing

Game Playing. Perfect decisions Heuristically based decisions Pruning search trees Games involving chance. What is a game?. Search problem with Initial state: board position and whose turn it is Successor function: What are possible moves from here? Terminal test: Is the game over?

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Game Playing

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  1. Game Playing • Perfect decisions • Heuristically based decisions • Pruning search trees • Games involving chance

  2. What is a game? • Search problem with • Initial state: board position and whose turn it is • Successor function: What are possible moves from here? • Terminal test: Is the game over? • Utility function: How good is this terminal state?

  3. Differences from problem solving • Multiagent environment • Opponent makes own choices! • Playing quickly may be important – need a good way of approximating solutions and improving search

  4. Starting point:Look at entire tree

  5. Simple game • Let’s play a game! • Motivate minimax

  6. Minimax Decision • Assign a utility value to each possible ending • Assures best possible ending, assuming opponent also plays perfectly • opponent tries to give you worst possible ending • Depth-first search tree traversal that updates utility values as it recurses back up the tree

  7. Simple game for example:Minimax decision MAX (player) MIN(opponent) 3 12 8 2 4 6 14 5 2

  8. Simple game for example:Minimax decision 3 MAX (player) MIN(opponent) 3 2 2 3 12 8 2 4 6 14 5 2

  9. Properties of Minimax • Time complexity • O(bm) • Space complexity • O(bm) (or O(m) if you can just generate next successor) • Same complexity as depth-first search

  10. Multiplayer games • Same strategy exactly, but each node has a utility for each player involved • Assume that each player maximizes own utility at each node

  11. Typical tree size • For chess, b ~ 35, m ~ 100 for a “reasonable” game • completely intractable!

  12. So what can you do? • Cutoff search early and apply a heuristic evaluation function • Evaluation function can represent point values to pieces, board position, and/or other characteristics • Evaluation function represents in some sense “probability” of winning • In practice, evaluation function is often a weighted sum

  13. When do you cutoff search? • Most straightforward: depth limit • ... or even iterative deepening • Bad in some cases • What if just beyond depth limit, catastrophic move happens? • One fix: only apply evaluation function to quiescent moves, i.e. unlikely to have wild swings in evaluation function • Example: no pieces about to be captured • Run test on state – if not quiescent, run a quiescence search for a nearby suitable state

  14. Horizon Effect • One piece is about to transform the game • e.g. pawn becoming queen • Opponent can prevent this for a long time, but not forever • Minimax places this stellar move “beyond the horizon” • Procrastination • Resolved (somewhat) with singular extensions • Go much deeper on best moves • Related to quiescent search

  15. How much lookahead for chess? • Ply = half-move • Human novice: 4 ply • Typical PC, human master: 8 ply • Deep Blue, Deep Fritz: 10-20 ply • Kasparov, Kramnik: 20-30 ply but only on select strategies • But if b=35, m = 10 (for example): • Time ~ O(bm) = 3510 ~ 3.5 x 1011 • Need to cut this down

  16. Alpha-Beta Pruning: Example MAX (player) MIN(opponent) 3 3 12 8 2

  17. Alpha-Beta Pruning: Example 3 MAX (player) • Stop right here whenevaluating this node: • opponent takesminimum of these nodes, • player will take maximumof nodes above MIN(opponent) 3 3 12 8 2

  18. Alpha-Beta Pruning: Concept If m > n, Player wouldchoose the m-node toget a guaranteed utilityof at least mn-node would never bereached, stop evaluationof n-node as soon as youfind child with smallerutility m n

  19. Alpha-Beta Pruning: Concept If m < n, Opponent wouldchoose the m-node toget a guaranteed utilityof at mn-node would never bereached, stop evaluation ofn-node as soon as you find a child > m m n

  20. The Alpha and the Beta • At any given point in time… • a = largest utility found so far for MAX • b = smallest utility found so far for MIN

  21. A: a = -inf, b = inf B: a = -inf, b = inf C: a = -inf,b = inf D: a = -inf,b = inf E: a = 10,b = 10 utility = 10 Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  22. A: a = -inf, b = inf B: a = -inf, b = inf C: a = -inf,b = inf D: a = -inf,b = 10 E: a = 10,b = 10 Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  23. A: a = -inf, b = inf B: a = -inf, b = inf C: a = -inf,b = inf D: a = -inf,b = 10 F: a = 11,b = 11 Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  24. A: a = -inf, b = inf B: a = -inf, b = inf C: a = -inf,b = inf D: a = -inf,b = 10 utility = 10 F: a = 11,b = 11 utility = 11 Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  25. A: a = -inf, b = inf B: a = -inf, b = inf C: a = 10,b = inf D: a = -inf,b = 10 utility = 10 Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  26. A: a = -inf, b = inf B: a = -inf, b = inf C: a = 10,b = inf G: a = 10,b = inf Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  27. A: a = -inf, b = inf B: a = -inf, b = inf C: a = 10,b = inf G: a = 10,b = inf H: a = 9,b = 9 utility = 9 Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  28. A: a = -inf, b = inf B: a = -inf, b = inf C: a = 10,b = inf G: a = 10,b = 9 utility = ? H: a = 9,b = 9 At an opponent node, with a > b : Stop here and backtrack (never visit I) Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  29. A: a = -inf, b = inf B: a = -inf, b = inf C: a = 10,b = inf utility = 10 G: a = 10,b = 9 utility = ? Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  30. A: a = -inf, b = inf B: a = -inf, b = 10 C: a = 10,b = inf utility = 10 Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  31. A: a = -inf, b = inf B: a = -inf, b = 10 J: a = -inf,b = 10 ... and so on! Originally from http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/search/alpha-beta.html

  32. How effective is alpha-beta in practice? • Pruning does not affect final result • With some extra heuristics (good move ordering): • Branching factor becomes b1/2 • 35  6 • Can look ahead twice as far for same cost • Can easily reach depth 8 and play good chess

  33. Deterministic games today • Checkers: Chinook ended 40­year­reign of human world champion Marion Tinsley in 1994. Used an endgame database defining perfect play for all positions involving 8 or fewer pieces on the board, a total of 443,748,401,247 positions. • Othello: human champions refuse to compete against computers, who are too good. • Go: human champions refuse to compete against computers, who are too bad. In go, b > 300, so most programs use pattern knowledge bases to suggest plausible moves.

  34. Deterministic games today • Chess: Deep Blue defeated human world champion Gary Kasparov in a six­game match in 1997. Deep Blue searched 197 million positions per second, used very sophisticated evaluation, and undisclosed methods for extending some lines of search up to 40 ply.

  35. More on Deep Blue • Garry Kasparov, world champ, beat IBM’s Deep Blue in 1996 • In 1997, played a rematch • Game 1: Kasparov won • Game 2: Kasparov resigned when he could have had a draw • Game 3: Draw • Game 4: Draw • Game 5: Draw • Game 6: Kasparov made some bad mistakes, resigned Info from http://www.mark-weeks.com/chess/97dk$$.htm

  36. Kasparov said... • “Unfortunately, I based my preparation for this match ... on the conventional wisdom of what would constitute good anti-computer strategy.Conventional wisdom is -- or was until the end of this match -- to avoid early confrontations, play a slow game, try to out-maneuver the machine, force positional mistakes, and then, when the climax comes, not lose your concentration and not make any tactical mistakes.It was my bad luck that this strategy worked perfectly in Game 1 -- but never again for the rest of the match. By the middle of the match, I found myself unprepared for what turned out to be a totally new kind of intellectual challenge. http://www.cs.vu.nl/~aske/db.html

  37. Some technical details on Deep Blue • 32-node IBM RS/6000 supercomputer • Each node had a Power Two Super Chip (P2SC) Processor and 8 specialized chess processors • Total of 256 chess processors working in parallel • Could calculate 60 billion moves in 3 minutes • Evaluation function (tuned via neural networks) considers • material: how much pieces are worth • position: how many safe squares can pieces attack • king safety: some measure of king safety • tempo: have you accomplished little while opponent has gotten better position? • Written in C under AIX Operating System • Uses MPI to pass messages between nodes http://www.research.ibm.com/deepblue/meet/html/d.3.3a.html

  38. Deep Fritz • Played world champion Vladimir Kramnik in 2002 • More “fair” contest: Kramnik could play with Deep Fritz software in advance • Ran on $40k 8 processor Compaq server running Windows XP, essentially same software sold for normal computers • Searched fewer moves than Deep Blue per second, but heuristics were better Pic from ww.chess.gr

  39. Kramnik starts strong • Game 1: Kramnik black, Fritz white • Typically play to a draw when playing black. Fritz ended up in “Berlin endgame” which Kramnik knows better than anyone. Kramnik sealed a draw. • Game 2: Kramnik white, Fritz black • Fritz makes a dreadfully stupid mistake that beginners don’t even make. Kramnik wins. http://www.chessbase.com/images2/2002/bahrain/games/bahrain2.htm • Game 3: Kramnik black, Fritz black • Fritz traded queens, but couldn’t fight this kind of battle, Kramnik wins

  40. But later… • Game 4: Kramnik white, Fritz black • Kramnik ended up in a long, drawn out ending resulting in a draw • Game 5: Kramnik black, Fritz white • Deep in a difficult game, Kramnik makes worst mistake of career and resigns, Fritz wins • Game 6: Kramnik white, Fritz black • Kramnik resigns, but analysis after the fact hasn’t found a certain win for black, Fritz wins • Game 7: Kramnik black, Fritz white • Kramnik plays to draw • Game 8: Kramnik white, Fritz black • 21 moves in, Kramnik can’t do anything, offers draw and Fritz accepts

  41. Alpha-Beta Pruning:Coding It (defun max-value (state alpha beta) (let ((node-value 0)) (if (cutoff-test state) (evaluate state) (dolist (new-state (neighbors state) nil) (setf node-value (min-value new-state alpha beta)) (setf alpha (max alpha node-value)) (if (>= alpha beta) (return beta))) alpha)))

  42. Alpha-Beta Pruning:Coding It (defun min-value (state alpha beta) (let ((node-value 0)) (if (cutoff-test state) (evaluate state) (dolist (new-state (neighbors state) nil) (setf node-value (max-value new-state alpha beta)) (setf beta (min beta node-value)) (if (<= beta alpha) (return alpha))) beta)))

  43. Nondeterminstic Games • Games with an element of chance (e.g., dice, drawing cards) like backgammon, Risk, RoboRally, Magic, etc. • Add chance nodes to tree

  44. Example with coin flip instead of dice (simple) 0.5 0.5 0.5 0.5 2 4 7 4 6 0 5 -2

  45. Example with coin flip instead of dice (simple) 3 3 -1 0.5 0.5 0.5 0.5 2 4 0 -2 2 4 7 4 6 0 5 -2

  46. Expectiminimax Methodology • For each chance node, determine expected value • Evaluation function should be linear with value, otherwise expected value calculations are wrong • Evaluation should be linearly proportional to expected payoff • Complexity: O(bmnm), where n=number of random states (distinct dice rolls) • Alpha-beta pruning can be done • Requires a bounded evaluation function • Need to calculate upper / lower bounds on utilities • Less effective

  47. Real World • Most gaming systems start with these concepts, then apply various hacks and tricks to get around computability problems • Databases of stored game configurations • Learning (coming up next): Chapter 18

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