AI Lesson 08

Module
3
Problem Solving
using Search-
(Two agent)
Version 2 CSE IIT, Kharagpur

Lesson
8
Two agent games :
alpha beta pruning

3.5 Alpha-Beta Pruning
ALPHA-BETA pruning is a method that reduces the number of nodes explored in
Minimax strategy. It reduces the time required for the search and it must be restricted so
that no time is to be wasted searching moves that are obviously bad for the current player.
The exact implementation of alpha-beta keeps track of the best move for each side as it
moves throughout the tree.

We proceed in the same (preorder) way as for the minimax algorithm. For the MIN
nodes, the score computed starts with +infinity and decreases with time. For MAX
nodes, scores computed starts with -infinity and increase with time.

The efficiency of the Alpha-Beta procedure depends on the order in which successors of a
node are examined. If we were lucky, at a MIN node we would always consider the nodes
in order from low to high score and at a MAX node the nodes in order from high to low
score. In general it can be shown that in the most favorable circumstances the alpha-beta
search opens as many leaves as minimax on a game tree with double its depth.

Here is an example of Alpha-Beta search:

3.5.1 Alpha-Beta algorithm:

The algorithm maintains two values, alpha and beta, which represent the minimum
score that the maximizing player is assured of and the maximum score that the
minimizing player is assured of respectively. Initially alpha is negative infinity and
beta is positive infinity. As the recursion progresses the "window" becomes smaller.


When beta becomes less than alpha, it means that the current position cannot be the
result of best play by both players and hence need not be explored further.

Pseudocode for the alpha-beta algorithm is given below.

evaluate (node, alpha, beta)
if node is a leaf
return the heuristic value of node
if node is a minimizing node
for each child of node
beta = min (beta, evaluate (child, alpha, beta))
if beta <= alpha
return beta
return beta
if node is a maximizing node
for each child of node
alpha = max (alpha, evaluate (child, alpha, beta))
if beta <= alpha
return alpha
return alpha

Questions
1. Suppose you and a friend of yours are playing a board game. It is your turn to move,
and the tree below represents your situation. The values of the evaluation function at
the leaf nodes are shown in the tree. Note that in this tree not all leaf nodes are at the
same level. Use the minimax procedure to select your next move. Show your work on
the tree below.


2. In the above tree use the minimax procedure along with alpha-beta pruning to select
the next move. Mark the nodes that don’t need to be evaluated.

3. Consider the game of tic-tac-toe. Assume that X is the MAX player. Let the utility of a
win for X be 10, a loss for X be -10, and a draw be 0.

a) Given the game board board1 below where it is X’s turn to play next, show the entire
game tree. Mark the utilities of each terminal state and use the minimax algorithm to
calculate the optimal move.

b) Given the game board board2 below where it is X’s turn to play next, show the game
tree with a cut-off depth of two ply (i.e., stop after each player makes one move). Use the
following evaluation function on all leaf nodes:

Eval(s) = 10X3(s) + 3X2(s) + X1(s) – (10O3(s) + 3O2(s) + O1(s))

where we define Xn(s) as the number of rows, columns, or diagonals in state s with
exactly n X’s and no O’s, and similarly define On(s) as the number of rows, columns, or
diagonals in state s with exactly n O’s and no X’s. Use the minimax algorithm to
determine X’s best move.

Solution
1. The second child of the root node as your next move since that child produces the
highest gain.

2. Nodes marked with an "X" are not evaluated when alpha-beta pruning is used. None of
the nodes in the subtree rooted at the node marked with an "X" in the third level of the
tree are evaluated.


3.a. The game tree is shown below


3.b. The values are shown below


AI Lesson 08

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