Dynamic Difficulty Adjustment (DDA) of Game AI aims at creating a satisfactory game experience by dynamically adjusting intelligence of game opponents. It can provide a level of challenge that is tailored to the player's personal ability. The Monte-Carlo Tree Search (MCTS) algorithm can be applied to generate intelligence of non-player characters (NPCs) in video games. And the performance of the NPCs controlled by MCTS can be adjusted by modulating the simulation time of MCTS.Hence the approach of DDA based on MCTS is proposed based on the application of MCTS. In this paper, the prey and predator game genre of Pac-Man is used as a test-bed, the process of creating DDA based on MCTS is demonstrated and the feasibility of this approach is validated. Furthermore, to increase the computational efficiency, an alternative approach of creating DDA based on knowledge from MCTS is also proposed and discussed.

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Adaptive Game AI improves adaptability of opponent AI with the challenge level of the gameplay; as a result the entertainment of game is augmented. Opponent game AI is usually implemented by scripted rules in video games. However MCT (Monte-Carlo for Trees), a most updated algorithm which perform excellent in computer go can also be used to achieve excellent result to control non-player characters (NPCs) in video games. In this paper, the prey and predator game genre of Pac-Man is used as a test-bed, the basic principle of MCT is presented, and the effectiveness of its application to game AI development is demonstrated. Furthermore, in order to reduce the computation intensiveness of Monte-Carlo, ANN (Artificial Neural Network) is used to produce the intelligence of game opponent with the data collected from Monte-Carlo method. The effectiveness and efficiency of the process is proved.