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Games. CPSC 386 Artificial Intelligence Ellen Walker Hiram College. Why Games?. Small number of rules Well-defined knowledge set Easy to evaluate performance Large search spaces (too large for exhaustive search) Fame & Fortune, e.g. Chess.

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CPSC 386 Artificial Intelligence

Ellen Walker

Hiram College

why games
Why Games?
  • Small number of rules
  • Well-defined knowledge set
  • Easy to evaluate performance
  • Large search spaces (too large for exhaustive search)
  • Fame & Fortune, e.g. Chess
example games best computer players sec 6 6 w updates
Example Games & Best Computer Players (sec. 6.6 w/updates)
  • Chess - Deep Blue (beat Kasparov); Deep Junior (tied Kasparov); Hydra (scheduled to play British champion for 80,000 pounds)
  • Checkers - Chinook (world champion)
  • Go (Goemate, Go4++ rated “weak amateur”)
  • Othello - Iago (world championship level), Logistello (defeated world champion, now retired)
  • Backgammon - TD-Gammon (neural network that learns to play using “reinforcement learning”)
properties of games
Properties of Games
  • Two-Player
  • Zero-sum
    • If it’s good for one player, it’s bad for the opponent and vice versa
  • Perfect information
    • All relevant information is apparent to both players (no hidden cards)
game as search problem
Game as Search Problem
  • State space search
    • Each potential board or game position is a state
    • Each possible move is an operation
    • Space can be BIG:
      • large branching factor (chess avg. 35)
      • deep search for game (chess avg. 50 ply)
  • Components of any search technique
    • Move generator (successor function)
    • Terminal test (end of game?)
    • Utility function (win, lose or draw?)
game tree
Game Tree
  • Root is initial state
  • Next level is all of first player’s moves
  • Next level is all of second player’s moves
  • Example: Tic Tac Toe
    • Root: 9 blank squares
    • Level 1: 3 different boards (corner, center and edge X)
    • Level 2 below center: 2 different boards (corner, edge)
    • Etc.
  • Utility function: win for X is 1, win for O is -1
    • X is Maximizer, O is minimizer
minimax strategy
Minimax Strategy
  • Max’s goal: get to 1
  • Min’s goal: get to -1
  • Max’s strategy
    • Choose moves that will lead to a win, even though min is trying to block
  • Minimax value of a node (backed up value):
    • If N is terminal, use the utility value
    • If N is a Max move, take max of successors
    • If N is a Min move, take min of successors
minimax algorithm
Minimax Algorithm
  • Depth-first search to bottom of tree
  • As search “unwinds”, compute backed up values
  • Backed-up value of root determines which step to take.
  • Assumes:
    • Both players are playing this strategy (optimally)
    • Tree is small enough to search completely
alpha beta pruning
Alpha-Beta Pruning
  • We don’t really have to look at all subtrees!
  • Recognize when a position can never be chosen in minimax no matter what its children are
    • Max (3, Min(2,x,y) …) is always ≥ 3
    • Min (2, Max(3,x,y) …) is always ≤ 2
    • We know this without knowing x and y!
alpha beta pruning1
Alpha-Beta Pruning
  • Alpha = the value of the best choice we’ve found so far for MAX (highest)
  • Beta = the value of the best choice we’ve found so far for MIN (lowest)
  • When maximizing, cut off values lower than Alpha
  • When minimizing, cut off values greater than Beta
alpha beta example
Alpha-Beta Example














notes on alpha beta pruning
Notes on Alpha-Beta Pruning
  • Effectiveness depends on order of successors (middle vs. last node of 2-ply example)
  • If we can evaluate best successor first, search is O(bd/2) instead of O(bd)
  • This means that in the same amount of time, alpha-beta search can search twice as deep!
optimizing minimax search
Optimizing Minimax Search
  • Use alpha-beta cutoffs
    • Evaluate most promising moves first
  • Remember prior positions, reuse their backed-up values
    • Transposition table (like closed list in A*)
  • Avoid generating equivalent states (e.g. 4 different first corner moves in tic tac toe)
  • But, we still can’t search a game like chess to the end!
when you can t search to the end
When you can’t search to the end
  • Replace terminal test (end of game) by cutoff test (don’t search deeper)
  • Replace utility function (win/lose/draw) by heuristic evaluation function that estimates results on the best path below this board
    • Like A* search, good evaluation functions mean good results (and vice versa)
  • Replace move generator by plausible move generator (don’t consider “dumb” moves)
good evaluation functions
Good evaluation functions…
  • Order terminal states in the same order as the utility function
  • Don’t take too long (we want to search as deep as possible in limited time)
  • Should be as accurate as possible (estimate chances of winning from that position…)
    • Human knowledge (e.g. material value)
    • Known solution (e.g. endgame)
    • Pre-searched examples (take features, average value of endgame of all games with that feature)
how deep to search
How Deep to Search?
  • Until time runs out (the original application of Iterative Deepening!)
  • Until values don’t seem to change (quiescence)
  • Deep enough to avoid horizon effect (delaying tactic to delay the inevitable beyond the depth of the search)
  • Singular extensions - search best (apparent) paths deeper than others
    • Tends to limit horizon effect, since these are the moves that will exhibit it