Alpha-beta pruning can be applied at any depth of a tree, and sometimes it not only prune the tree leaves but also entire sub-tree.
The two-parameter can be defined as:
Alpha: The best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞.
Beta: The best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞.
2. • we can sometimes cut the tree in half, computing the correct
minimax decision without examining every state by pruning large
parts of the tree that make no difference to the outcome.
• The particular technique we examine is called alpha–beta pruning
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10. Heuristic Alpha Beta search
• Apply heuristic evaluation function
• Replace terminal test by
• cut off test
11. • Features- chess coins no. of pawns,rook,bishop
• Expected value- For example, suppose our experience suggests that
82% of the states encountered in the two-pawns versus one-pawn
category lead to a win (utility +1); 2% to a loss (0), and 16% to a
draw(1/2) . Then a reasonable evaluation for states in the category is
the expected value: (0.82 × +1) + (0.02 × 0) + (0.16 × 1/2) = 0.90
12. • Material value: material value for each piece: each pawn is worth 1, a
knight or bishop is worth 3, a rook 5, and the queen 9.
• Other features such as “good pawn structure” and “king safety” might
be worth half a pawn, say.
• These feature values are then simply added up to obtain the
evaluation of the position.