Motion Gaming AI using Time Series Forecasting and Dynamic Difficulty Adjustment
Takahiro Kusano, Yunshi Liu, Pujana Paliyawan, Ruck Thawonmas, Tomohiro Harada · 2019 IEEE Conference on Games (CoG) · 2019
This paper proposes a motion gaming AI that encourages players to use their body parts in a well-balanced manner while promoting their player experience. The proposed AI is an enhanced version of our previous AI in two aspects. First, it uses time series forecasting to more precisely predict what actions the player will perform with respect to its candidate actions, based on which the amount of movement to be produced on each body part of the player against each of such candidates is derived; as in our previous work. the AI selects its action from those candidates with a goal of making the player’s movement of their body parts on both sides equal. Second, this AI employs Monte-Carlo tree search that finds candidate actions according to dynamic difficulty adjustment. Our results show that the proposed game AI outperforms our previous AI in terms of the player’s body-movement balancedness, enjoyment, engrossment, and personal gratification.