Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1986 Kluwer Academic Publishers
About this chapter
Cite this chapter
Christensen, J. (1986). Learning Static Evaluation Functions by Linear Regression. In: Machine Learning. The Kluwer International Series in Engineering and Computer Science, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2279-5_9
Download citation
DOI: https://doi.org/10.1007/978-1-4613-2279-5_9
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-9406-1
Online ISBN: 978-1-4613-2279-5
eBook Packages: Springer Book Archive