The purpose of this project is to construct a chess winnig possition classifier (or score estimator) based on some simple features (e.g. the number of pieces of each kind, number of squares controled by given side, numbers of checks that you can give in a possition and so on) The first goal of the project is to establish how far we can get with relatively cheap methods of position evaluation and simple data mining methods (eg. logistic regression, k-nearest neighbors, SVM, decision trees). The second goal is to use more advance machine learning techniques for recognition of winning position to emulate human behaviour of various levels of chess proficiency. This means we need good heuristic which will allow to narrow the range of positions to calculate in depth. There is a hope that modifying this heuristic from “more learnt†to “less learnt†we will obtain something similar to natural human levels.
