cs-171-07-Games and Adversarila Search.ppt

Game-Playing & Adversarial Search
This lecture topic:
Game-Playing & Adversarial Search (two lectures)
Chapter 5.1-5.5
Next lecture topic:
Constraint Satisfaction Problems (two lectures)
Chapter 6.1-6.4, except 6.3.3
(Please read lecture topic material before and after each lecture on that topic)

Overview
• Minimax Search with Perfect Decisions
– Impractical in most cases, but theoretical basis for analysis
• Minimax Search with Cut-off
– Replace terminal leaf utility by heuristic evaluation function
• Alpha-Beta Pruning
– The fact of the adversary leads to an advantage in search!
• Practical Considerations
– Redundant path elimination, look-up tables, etc.
• Game Search with Chance
– Expectiminimax search

You Will Be Expected to Know
• Basic definitions (section 5.1)
• Minimax optimal game search (5.2)
• Alpha-beta pruning (5.3)
• Evaluation functions, cutting off search (5.4.1, 5.4.2)
• Expectiminimax (5.5)

Types of Games
battleship
Kriegspiel
Not Considered: Physical games like tennis, croquet,
ice hockey, etc.
(but see “robot soccer” http://www.robocup.org/)

Typical assumptions
• Two agents whose actions alternate
• Utility values for each agent are the opposite of the other
– This creates the adversarial situation
• Fully observable environments
• In game theory terms:
– “Deterministic, turn-taking, zero-sum games of perfect information”
• Generalizes to stochastic games, multiple players, non zero-sum, etc.
• Compare to, e.g., “Prisoner’s Dilemma” (p. 666-668, R&N 3rd ed.)
– “Deterministic, NON-turn-taking, NON-zero-sum game of IMperfect
information”

Game tree (2-player, deterministic, turns)
How do we search this tree to find the optimal move?

Search versus Games
• Search – no adversary
– Solution is (heuristic) method for finding goal
– Heuristics and CSP techniques can find optimal solution
– Evaluation function: estimate of cost from start to goal through given node
– Examples: path planning, scheduling activities
• Games – adversary
– Solution is strategy
• strategy specifies move for every possible opponent reply.
– Time limits force an approximate solution
– Evaluation function: evaluate “goodness” of game position
– Examples: chess, checkers, Othello, backgammon

Games as Search
• Two players: MAX and MIN
• MAX moves first and they take turns until the game is over
– Winner gets reward, loser gets penalty.
– “Zero sum” means the sum of the reward and the penalty is a constant.
• Formal definition as a search problem:
– Initial state: Set-up specified by the rules, e.g., initial board configuration of chess.
– Player(s): Defines which player has the move in a state.
– Actions(s): Returns the set of legal moves in a state.
– Result(s,a): Transition model defines the result of a move.
– (2nd ed.: Successor function: list of (move,state) pairs specifying legal moves.)
– Terminal-Test(s): Is the game finished? True if finished, false otherwise.
– Utility function(s,p): Gives numerical value of terminal state s for player p.
• E.g., win (+1), lose (-1), and draw (0) in tic-tac-toe.
• E.g., win (+1), lose (0), and draw (1/2) in chess.
• MAX uses search tree to determine next move.

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, down to the leaves.
• 2. Apply utility (payoff) function to each leaf.
• 3. Back-up values from leaves through branch nodes:
– a Max node computes the Max of its child values
– a Min node computes the Min of its child values
• 4. At root: choose the move leading to the child of highest value.

Two-Ply Game Tree
The minimax decision
Minimax maximizes the utility for the worst-case outcome for max

Pseudocode for Minimax Algorithm
function MINIMAX-DECISION(state) returns an action
inputs: state, current state in game
return arg maxaACTIONS(state) MIN-VALUE(Result(state,a))
function MIN-VALUE(state) returns a utility value
if TERMINAL-TEST(state) then return UTILITY(state)
v  +∞
for a in ACTIONS(state) do
v  MIN(v,MAX-VALUE(Result(state,a)))
return v
function MAX-VALUE(state) returns a utility value
if TERMINAL-TEST(state) then return UTILITY(state)
v  −∞
for a in ACTIONS(state) do
v  MAX(v,MIN-VALUE(Result(state,a)))
return v

Properties of minimax
• Complete?
– Yes (if tree is finite).
• Optimal?
– Yes (against an optimal opponent).
– Can it be beaten by an opponent playing sub-optimally?
• No. (Why not?)
• Time complexity?
– O(bm)
• Space complexity?
– O(bm) (depth-first search, generate all actions at once)
– O(m) (backtracking search, generate actions one at a time)

Game Tree Size
• Tic-Tac-Toe
– b ≈ 5 legal actions per state on average, total of 9 plies in game.
• “ply” = one action by one player, “move” = two plies.
– 59 = 1,953,125
– 9! = 362,880 (Computer goes first)
– 8! = 40,320 (Computer goes second)
 exact solution quite reasonable
• Chess
– b ≈ 35 (approximate average branching factor)
– d ≈ 100 (depth of game tree for “typical” game)
– bd ≈ 35100 ≈ 10154 nodes!!
 exact solution completely infeasible
• It is usually impossible to develop the whole search tree.

(Static) Heuristic Evaluation Functions
• An Evaluation Function:
– Estimates how good the current board configuration is for a player.
– Typically, evaluate how good it is for the player, how good it is for
the opponent, then subtract the opponent’s score from the player’s.
– Often called “static” because it is called on a static board position.
– 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
– “Zero-sum game”

cs-171-07-Games and Adversarila Search.ppt

Applying MiniMax to tic-tac-toe
• The static heuristic evaluation function

Alpha-Beta Pruning
Exploiting the Fact of an Adversary
• If a position is provably bad:
– It is NO USE expending search time to find out exactly how bad
• If the adversary can force a bad position:
– It is NO USE expending search time to find out the good positions
that the adversary won’t let you achieve anyway
• Bad = not better than we already know we can achieve elsewhere.
• Contrast normal search:
– ANY node might be a winner.
– ALL nodes must be considered.
– (A* avoids this through knowledge, i.e., heuristics)

Tic-Tac-Toe Example with Alpha-Beta Pruning
Backup Values

Another Alpha-Beta Example
(−∞, +∞)
(−∞,+∞)
Range of possible values
Do DF-search until first leaf

Alpha-Beta Example (continued)
(−∞,3]
(−∞,+∞)

[3,+∞)
[3,3]

(−∞,2]
[3,+∞)
[3,3]
This node is
worse for MAX

(−∞,2]
[3,14]
[3,3] (−∞,14]
,

(−∞,2]
[3,5]
[3,3] (−∞,5]
,

[2,2]
(−∞,2]
[3,3]
[3,3]

General alpha-beta pruning
• Consider a node n in the tree ---
• If player has a better choice at:
– Parent node of n
– Or any choice point further up
• Then n will never be reached in play.
• Hence, when that much is known
about n, it can be pruned.

Alpha-beta Algorithm
• Depth first search
– only considers nodes along a single path from root at any time
a = highest-value choice found at any choice point of path for MAX
(initially, a = −infinity)
b = lowest-value choice found at any choice point of path for MIN
(initially, b = +infinity)
• Pass current values of a and b down to child nodes during search.
• Update values of a and b during search:
– MAX updates a at MAX nodes
– MIN updates b at MIN nodes
• Prune remaining branches at a node when a ≥ b

When to Prune
• Prune whenever a ≥ b.
– Prune below a Max node whose alpha value becomes greater than
or equal to the beta value of its ancestors.
• Max nodes update alpha based on children’s returned values.
– Prune below a Min node whose beta value becomes less than or
equal to the alpha value of its ancestors.
• Min nodes update beta based on children’s returned values.

Alpha-Beta Example Revisited
a, b, initial values
Do DF-search until first leaf
a=−
b =+
a=−
b =+
a, b, passed to kids

MIN updates b, based on kids
a=−
b =+
a=−
b =3

a=−
b =3
MIN updates b, based on kids.
No change.
a=−
b =+

MAX updates a, based on kids.
a=3
b =+
3 is returned
as node value.

a=3
b =+
a=3
b =+

a=3
b =+
a=3
b =2
MIN updates b,
based on kids.

a=3
b =2
a ≥ b,
so prune.
a=3
b =+

2 is returned
as node value.
MAX updates a, based on kids.
No change. a=3
b =+

,
a=3
b =+
a=3
b =+

,
a=3
b =14
a=3
b =+
MIN updates b,
based on kids.

,
a=3
b =5
a=3
b =+
MIN updates b,
based on kids.

a=3
b =+ 2 is returned
as node value.
2

Max calculates the
same node value, and
makes the same move!
2

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 child (i.e., evaluated first)
– in practice, performance is closer to best rather than worst-case
– E.g., sort moves by the remembered move values found last time.
– E.g., expand captures first, then threats, then forward moves, etc.
– E.g., run Iterative Deepening search, sort by value last iteration.
• In practice often get O(b(d/2)) rather than O(bd)
– this is the same as having a branching factor of sqrt(b),
• (sqrt(b))d = b(d/2),i.e., we effectively go 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

Final Comments about Alpha-Beta Pruning
• Pruning does not affect final results
• Entire subtrees can be pruned.
• Good move ordering improves effectiveness of pruning
• Repeated states are again possible.
– Store them in memory = transposition table

Example
3 4 1 2 7 8 5 6
-which nodes can be pruned?

Answer to Example
3 4 1 2 7 8 5 6
Answer: NONE! Because the most favorable nodes for both are
explored last (i.e., in the diagram, are on the right-hand side).
Max
Min
Max

Second Example
(the exact mirror image of the first example)
6 5 8 7 2 1 3 4

Answer to Second Example
(the exact mirror image of the first example)
6 5 8 7 2 1 3 4
Min
Max
Max
Answer: LOTS! Because the most favorable nodes for both are
explored first (i.e., in the diagram, are on the left-hand side).

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

Heuristics and Game Tree Search: limited horizon
• 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
because it has a “limited horizon”.
– there are heuristics to try to follow certain branches more deeply to
detect 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

Eliminate Redundant Nodes
• On average, each board position appears in the search tree
approximately ~10150 / ~1040 ≈ 10100 times.
=> Vastly redundant search effort.
• Can’t remember all nodes (too many).
=> Can’t eliminate all redundant nodes.
• However, some short move sequences provably lead to a
redundant position.
– These can be deleted dynamically with no memory cost
• Example:
1. P-QR4 P-QR4; 2. P-KR4 P-KR4
leads to the same position as
1. P-QR4 P-KR4; 2. P-KR4 P-QR4

The State of Play
• Checkers:
– Chinook ended 40-year-reign of human world champion Marion
Tinsley in 1994.
• Chess:
– Deep Blue defeated human world champion Garry Kasparov in a
six-game match in 1997.
• Othello:
– human champions refuse to compete against computers: they are
too good.
• Go:
– human champions refuse to compete against computers: they are
too bad
– b > 300 (!)
• See (e.g.) http://www.cs.ualberta.ca/~games/ for more information

Deep Blue
• 1957: Herbert Simon
– “within 10 years a computer will beat the world chess champion”
• 1997: Deep Blue beats Kasparov
• Parallel machine with 30 processors for “software” and 480 VLSI
processors for “hardware search”
• Searched 126 million nodes per second on average
– Generated up to 30 billion positions per move
– Reached depth 14 routinely
• Uses iterative-deepening alpha-beta search with transpositioning
– Can explore beyond depth-limit for interesting moves

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.

cs-171-07-Games and Adversarila Search.ppt

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