Decision Quality As a Function of Search Depth on Game Trees
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Almost all game tree search procedures used in artificial intelligence are variants on minimaxing. Until recently, it was almost universally believed that searching deeper on the game tree with such procedures would in general yield a better decision. However, recent investigations have revealed the existence of many game trees and evaluation functions which are 'pathological' in the sense that searching deeper consistently degrades the decision. This paper extends these investigations in two ways. First, it is shown that whenever the evaluation function satisfies certain properties, pathology will occur on any game tree of high enough constant branching factor. This result, together with Monte Carlo studies on actual games, gives insight into the causes of pathology. Second, an investigation is made of a possible cure for pathology: a probabilistic decision procedure which does not use minimaxing. Under some conditions, this procedure gives results superior to minimaxing.