To create DDA by the Approach of ANN from UCT-Created Data
Dynamic Difficulty Adjustment (DDA) can adjust game difficulty level dynamically; so it generates a tailor-made experience for each gamer. If a game is too easy, the gamer will feel bored; if it is too hard, the gamer will become frustrated. DDA is a mechanism to overcome this dilemma and augment the entertainment of a game by dynamically adjusting the parameters, scenarios and behaviors in the game in real-time based on the gamers personal ability 1. We use Upper Confidence bound for Trees (UCT) to create the training data, and then train the Artificial Neural Networks (ANN) off-line with that data 2. Finally, we derive DDA from ANN approach. In this paper, the prey and predator game genre of Pac-Man is utilized as a test-bed, the procedure of training ANN is shown, and the feasibility of applying DDA to game artificial intelligence (AI) development is demonstrated.
DDA ANN UCT AI pac-man
Xinyu Li Suqju He Yue Dong Qing Liu Xiao Liu Yiwen Fu Zhiyuan Shi Wan Huang
International School Beijing University of Posts and Telecommunications Beijing, China International School School of Software Engineering Beijing University of Posts and Telecommunicatio
国际会议
太原
英文
475-478
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）