基于自适应神经控制对手的动态难度调整实现

Wan Huang, Suoju He, Delin Chang, Y. Hao
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引用次数: 5

摘要

不同技能的游戏玩家希望在游戏中体验到不同的挑战关卡,并从中获得乐趣和满足感。因此,智能的游戏对手可以适应不同的玩家策略和不同的玩家技能。传统的难度调整设置对手的地位往往会让玩家产生一种被欺骗的感觉,这并不能完全满足玩家。在本文中,我们证明了通过计算智能(CI)方法,包括蒙特卡洛树搜索(MCTS)和树的上置信度界(UCT)算法来调整对手的挑战水平,可以实现动态难度调整(DDA),使玩家的游戏体验更加个性化。然而,CI方法的一个特点是计算强度,它可能只适用于离线游戏。相比之下,另一种被提出的DDA方法——自适应人工神经网络(ANN)控制对手可以将动态难度的应用扩展到在线领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic difficulty adjustment realization based on adaptive neuro-controlled game opponent
Game players of different skills expect different challenge levels in game to be filled with enjoyment and fulfillment. Thus intelligent game opponent can be made adaptive to match different player strategies and different player skills. Traditional difficulty adjustment setting the status of the opponent often fills players with a feeling of being cheated, which cannot perfectly satisfy the player. In this paper, we demonstrate that by adjusting the challenge level of opponents through Computational Intelligence (CI) approach including Monte Carlo Tree Search (MCTS) and Upper Confidence bound for Trees (UCT) algorithms, we can realize Dynamic Difficulty Adjustment (DDA) and make players' game experience more personalized. However, as one character of CI approach is computational intensiveness, it may only be practical for offline game. Compared to that, another proposed DDA approach: adaptive Artificial Neural Network (ANN) controlled opponents can extend dynamic difficulty application to online field.
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