Lecture 6
Game Playing, Continued
CSE 573
Artificial Intelligence I
Henry Kautz
Fall 2001
Today
•	Preview: Learning in Games
•	Pathological Cases of Minimax
•	Alpha-Beta Pruning

Finish reading R&N chapter 5
Next week:
•	Constraint satisfaction: R&N 3.7
•	Logic: R&N 6-7

Preview: Learning in Games
Modify weights so that evaluation of root is closer to minimax value – minimize difference 
•	Samuel’s Checkers Program (1955!)
•	Temporal-Difference Learning (Sutton & Barto 1988)
•	NeuroChess (Thrun 1995)
Learning from Experts
Alternative: learn from human players
•	Find weights that minimize least-squared error between choices made by V and choices made by grandmasters
•	Deep Blue
Pathological Game Trees
Nau (1980) – 
•	Model: static value of each (non-terminal) node is an (independent) noisy approximation of true GT value
•	The deeper you search – the more chances of minimax being wrong due to noise
•	Approaches random guessing!
•	One way to avoid: evaluation function must become more accurate on deeper nodes (i.e. later in the game)