Optimizing player´s satisfaction through DDA of game AI by UCT for the Game Dead-End

Dealing with players of different skill levels is a key issue for game developers. A major concern for the game developers is to dynamically adjust the difficulty for different players so as to keep them interested in the game. In this paper, we propose “DDA by time-constrained-UCT” to generate intelligent agents to dynamically adapt to the variant capacities of different players. This UCT-based DDA can adjust the game´s challenge level by tuning the simulation time of UCT-controlled NPC. However, this approach is not suitable for network game because it consumes a lot storage resource for computation. So we further propose “ANN-from-time-constrained-UCT”, where the data acquired by UCT is applied for training the Artificial Neural Network (ANN) to control the opponents.