Data Mining in Adversarial Search-Players Movement Prediction in Connect 4 Games

摘要

Knowledge Discovery in Databases (KDD) is a major innovation in knowledge extraction This knowledge can be extracted to recognize patterns or behaviors. Board games playing patterns are a concise experiment on testing data mining methods in order to find such patterns and behaviors. In this work a Connect-4 game is simulated with several distinct players with different characteristics. Most of these distinct players have intelligent game playing abilities, whereas others are simpler and play by very simple rules. The work uses three different datamining algorithms in order to classify the players and their moves. Analyzing the results achieved we can conclude that General Linear Model leads to better results in terms of accuracy, class precision and class recall