Adversarial Search Chapter 6 Section 1 – 4 The Master vs Machine: A Video Outline Optimal decisions α-β pruning Imperfect, real-time decisions Games vs. search problems "Unpredictable" opponent specifying a move for every possible opponent reply
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Section 1 – 4
The Master vs Machine: A Video
doubles depth of search
If v is worse than α, max will avoid it
prune that branch
Define β similarly for minWhy is it called α-β?
Suppose we have 100 secs, explore 104 nodes/sec106nodes per move
e.g., depth limit (perhaps add quiescence search)
= estimated desirability of position
Eval(s) = w1 f1(s) + w2 f2(s) + … + wn fn(s)
f1(s) = (number of white queens) – (number of black queens), etc.
MinimaxCutoff is identical to MinimaxValue except
Does it work in practice?
bm = 106, b=35 m=4
4-ply lookahead is a hopeless chess player!