TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Learning to Play Chess Using Temporal Differences
Machine Learning
From Simple Features to Sophisticated Evaluation Functions
CG '98 Proceedings of the First International Conference on Computers and Games
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
| Hi-index | 0.00 |
Constructing evaluation functions with high accuracy is one of the critical factors in computer game players. This construction is usually done by hand, and deep knowledge of the game and much time to tune them are needed for the construction. To avoid these difficulties, automatic construction of the functions is useful. In this paper, we propose a new method to generate features for evaluation functions automatically based on game records. Evaluation features are built on simple features based on their frequency and mutual information. As an evaluation, we constructed evaluation functions for mate problems in shogi. The evaluation function automatically generated with several thousand evaluation features showed the accuracy of 74% in classifying positions into mate and non-mate.