Game Player Strategy Pattern Recognition and How UCT Algorithms Apply Pre-knowledge of Player´s Strategy to Improve Opponent AI

Player strategy pattern recognition (PSPR) is to apply pattern recognition and its approach to identification of player´s strategy during the gameplay. Correctly identified player´s strategy, which is called knowledge, could be used to improve game opponent AI which can be implemented by KB-UCT (knowledge-based upper confidence bound for trees). KB-UCT improves adaptability of game AI, the challenge level of the gameplay, and the performance of the opponent AI; as a result the entertainment of game is promoted. In this paper, the prey and predator game genre of dead end game is used as a test-bed. During the PSPR, classification algorithm of KNN (k-nearest neighbor) is chosen to analyze off-line data from the simulated gamers who are choosing different strategies. Based on the information from PSPR, the game AI is promoted through application of KB-UCT, in this case, domain knowledge is used for UCT tree pruning; as a result the performance of the opponent AI is enhanced.