Abstract
The heart of a chess program is its evaluation function, since it is this component which characterizes the style of play. A typical program evaluates a move by computing a weighted sum of the features that it considers. It is important to make the best selection of the relative weights of these features. They may be used not only to assess horizon nodes in the game tree, but also to order moves at the other nodes so that alpha-beta searching efficiency is improved. Standard optimization techniques can be used to find the weights, provided a suitable cost function can be found. This paper assesses the properties of several cost functions, and presents a method for finding optimum weightings for any set of features.
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