Learning Static Evaluation Functions by Linear Regression

Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 12)


We present a technique for learning the coefficients of a linear static evaluation function for two-person games based on playing experience. This is accomplished by using linear regression to modify the coefficients based on the difference between the static evaluation of a state and the value returned by a mini-max look-ahead search. In an initial experiment, the technique was used to learn relative weights for the different chess pieces.


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Copyright information

© Kluwer Academic Publishers 1986

Authors and Affiliations

  1. 1.Department of Computer ScienceColumbia UniversityNew YorkUSA

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