ICS-270a:Notes 5: 1

Notes 5: Game-Playing

ICS 270a Winter 2003

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Summary

• Computer programs which play 2-player games

– game-playing as search

– with the complication of an opponent

• General principles of game-playing and search

– evaluation functions

– minimax principle

– alpha-beta-pruning

– heuristic techniques

• Status of Game-Playing Systems

– in chess, checkers, backgammon, Othello, etc, computers routinely defeat leading world players

• Applications?

– think of “nature” as an opponent

– economics, war-gaming, medical drug treatment

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Chess Rating Scale

1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

1966 1971 1976 1981 1986 1991 1997

Ratings

Garry Kasparov (current World Champion) Deep Blue

Deep Thought

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Solving 2-players Games

• Two players, perfect information

• Examples:

– e.g., chess, checkers, tic-tac-toe

• configuration of the board = unique arrangement of “pieces”

• Statement of Game as a Search Problem

– States = board configurations

– Operators = legal moves

– Initial State = current configuration

– Goal = winning configuration

– payoff function = gives numerical value of outcome of the game

• A working example: Grundy's game

– Given a set of coins, a player takes a set and divides it into two unequal sets. The player who plays last, looses.

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Game Tree Representation

• New aspect to search problem– there’s an opponent we cannot control– how can we handle this?

SComputer Moves

OpponentMoves

ComputerMoves

G Possible Goal Statelower in Tree (winning situation for computer)

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Game Trees

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Game Trees

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Grundy’s Game

• Search Tree: represents Max moves.

• Goal: evaluate root node-value of game

• 0 - loss

• 1 - win

• In complex games search to termination is impossible. Rather:

• Find a first good move.

• Do it, wait for Min’s response

• Find a good move from new state

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Grundy’s game - special case of nim

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An optimal procedure: The Min-Max method

• Designed to find the optimal strategy for Max and find best move:

– 1. Generate the whole game tree to leaves

– 2. Apply utility (payoff) function to leaves

– 3. Back-up values from leaves toward the root:

• a Max node computes the max of its child values

• a Min node computes the Min of its child values

– 4. When value reaches the root: choose max value and the corresponding move.

• However: It is impossible to develop the whole search tree, instead

develop part of the tree and evaluate promise of leaves using a static

evaluation function.

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Complexity of Game Playing

• Imagine we could predict the opponent’s moves given each computer move

• How complex would search be in this case?

– worst case, it will be O(bd)

– Chess:

• b ~ 35 (average branching factor)

• d ~ 100 (depth of game tree for typical game)

• bd ~ 35100 ~10154 nodes!!

– Tic-Tac-Toe

• ~5 legal moves, total of 9 moves

• 59 = 1,953,125

• 9! = 362,880 (Computer goes first)

• 8! = 40,320 (Computer goes second)

• well-known games can produce enormous search trees

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Static (Heuristic) Evaluation Functions

• An Evaluation Function:

– estimates how good the current board configuration is for a player.

– Typically, one figures how good it is for the player, and how good it is for the opponent, and subtracts the opponents score from the players

– Othello: Number of white pieces - Number of black pieces

– Chess: Value of all white pieces - Value of all black pieces

• Typical values from -infinity (loss) to +infinity (win) or [-1, +1].

• If the board evaluation is X for a player, it’s -X for the opponent

• Example:

– Evaluating chess boards,

– Checkers

– Tic-tac-toe

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General Minimax Procedure on a Game Tree

For each move:

1. expand the game tree as far as possible

2. assign state evaluations at each open node

3. propagate upwards the minimax choicesif the parent is a Min node (opponent)

propagate up the minimum value of the childrenif the parent is a Max node (computer)

propagate up the maximum value of the children

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Minimax Principle

• “Assume the worst”

– say each configuration has an evaluation number

– high numbers favor the player (the computer)

• so we want to choose moves which maximize evaluation

– low numbers favor the opponent

• so they will choose moves which minimize evaluation

• Minimax Principle

– you (the computer) assume that the opponent will choose the minimizing move next (after your move)

– so you now choose the best move under this assumption

• i.e., the maximum (highest-value) option considering both your move and the opponent’s optimal move.

– we can extend this argument more than 2 moves ahead: we can search ahead as far as we can afford.

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Applying MiniMax to tic-tac-toe

• The static evaluation function heuristic

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Backup Values

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Pruning with Alpha/Beta

• In Min-Max there is a separation between node generation and evaluation.

Backup Values

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Alpha Beta Procedure

• Idea:

– Do Depth first search to generate partial game tree,

– Give static evaluation function to leaves,

– compute bound on internal nodes.

• Alpha, Beta bounds:

– Alpha value for Max node means that Max real value is at least alpha.

– Beta for Min node means that Min can guarantee a value below Beta.

• Computation:

– Alpha of a Max node is the maximum value of its seen children.

– Beta of a Min node is the lowest value seen of its child node .

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When to Prune

• Pruning

– Below a Min node whose beta value is lower than or equal to the alpha value of its ancestors.

– Below a Max node having an alpha value greater than or equal to the beta value of any of its Min nodes ancestors.

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Effectiveness of Alpha-Beta Search

• Worst-Case

– branches are ordered so that no pruning takes place. In this case alpha-beta gives no improvement over exhaustive search

• Best-Case

– each player’s best move is the left-most alternative (i.e., evaluated first)

– in practice, performance is closer to best rather than worst-case

• In practice often get O(b(d/2)) rather than O(bd)

– this is the same as having a branching factor of sqrt(b),

• since (sqrt(b))d = b(d/2)

• i.e., we have effectively gone from b to square root of b

– e.g., in chess go from b ~ 35 to b ~ 6

• this permits much deeper search in the same amount of time

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Iterative (Progressive) Deepening

• In real games, there is usually a time limit T on making a move

• How do we take this into account?

– using alpha-beta we cannot use “partial” results with any confidence unless the full breadth of the tree has been searched

– So, we could be conservative and set a conservative depth-limit which guarantees that we will find a move in time < T

• disadvantage is that we may finish early, could do more search

• In practice, iterative deepening search (IDS) is used

– IDS runs depth-first search with an increasing depth-limit

– when the clock runs out we use the solution found at the previous depth limit

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Heuristics and Game Tree Search

• The Horizon Effect

– sometimes there’s a major “effect” (such as a piece being captured) which is just “below” the depth to which the tree has been expanded

– the computer cannot see that this major event could happen

– it has a “limited horizon”

– there are heuristics to try to follow certain branches more deeply to detect to such important events

– this helps to avoid catastrophic losses due to “short-sightedness”

• Heuristics for Tree Exploration

– it may be better to explore some branches more deeply in the allotted time

– various heuristics exist to identify “promising” branches

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Computers can play GrandMaster Chess

• “Deep Blue” (IBM)

– parallel processor, 32 nodes

– each node has 8 dedicated VLSI “chess chips”

– each chip can search 200 million configurations/second

– uses minimax, alpha-beta, heuristics: can search to depth 14

– memorizes starts, end-games

– power based on speed and memory: no common sense

• Kasparov v. Deep Blue, May 1997

– 6 game full-regulation chess match (sponsored by ACM)

– Kasparov lost the match (2.5 to 3.5)

– a historic achievement for computer chess: the first time a computer is the best chess-player on the planet

• Note that Deep Blue plays by “brute-force”: there is relatively little which is similar to human intuition and cleverness

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Status of Computers in Other Games

• Checkers/Draughts– current world champion is Chinook, can beat any human– uses alpha-beta search

• Othello– computers can easily beat the world experts

• Backgammon– system which learns is ranked in the top 3 in the world– uses neural networks to learn from playing many many games against

itself

• Go– branching factor b ~ 360: very large!– $2 million prize for any system which can beat a world expert

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Summary

• Game playing is best modeled as a search problem

• Game trees represent alternate computer/opponent moves

• Evaluation functions estimate the quality of a given board configuration for the Max player.

• Minimax is a procedure which chooses moves by assuming that the opponent will always choose the move which is best for them

• Alpha-Beta is a procedure which can prune large parts of the search tree and allow search to go deeper

• For many well-known games, computer algorithms based on heuristic search match or out-perform human world experts.

• Reading: Nillson Chapter 12, R&N Chapter 5.

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Minimax Search Example

• Look ahead several turns (we’ll use 2 for now)

• Evaluate resulting board configurations

• The computer will make the move such that when the opponent makes his best move, the board configuration will be in the best position for the computer

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Propagating Minimax Values up the Game Tree

• Starting from the leaves

– Assign a value to the parent node as follows

• Children are Opponent’s moves: Minimum of all immediate children

• Children are Computer’s moves: Maximum of all immediate children

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Deeper Game Trees