| 引用本文: | 童浩,张清泉,袁博,王晗丁,刘佳琳.基于多目标集成剪枝的多样内容在线生成方法[J].控制理论与应用,2025,42(11):2352~2362.[点击复制] |
| TONG Hao,ZHANG Qing-quan,YUAN Bo,WANG Han-ding,LIU Jia-lin.Online diverse content generation via multi-objective ensemble pruning[J].Control Theory & Applications,2025,42(11):2352~2362.[点击复制] |
|
| 基于多目标集成剪枝的多样内容在线生成方法 |
| Online diverse content generation via multi-objective ensemble pruning |
| 摘要点击 1941 全文点击 121 投稿时间：2025-03-28 修订日期：2025-10-13 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.50126 |
| 2025,42(11):2352-2362 |
| 中文关键词 程序化内容生成 在线内容生成 多目标优化 演化算法 集成学习 视频游戏 |
| 英文关键词 procedural content generation online content generation multi-objective optimization evolutionary algo rithms ensemble learning video games |
| 基金项目 国家重点研发计划项目(2023YFE0106300),国家自然科学基金项目(62476119,62376202,62250710682)资助. |
|
| 中文摘要 |
| 在线生成多样内容是近年来程序化内容生成领域的新兴研究方向之一,不仅可以满足用户的不同偏好进
而提升用户体验,也可以为人工智能算法的训练和测试提供海量的场景集和问题集.近期研究提出了基于负相关
集成强化学习的多样内容在线生成方法,但此类方法无法有效地匹配不同用户的偏好.此外,训练与部署集成模型
中的个体学习器需要消耗大量的计算资源.针对这两个问题,在负相关集成强化学习的框架下,本文提出了基于多
目标集成剪枝的多样内容在线生成方法.该方法通过多目标优化算法搜索模型集成的权重,使获得的集成模型不
仅能够有效地匹配给定的用户偏好,还能提供权衡模型性能与计算资源消耗的帕累托集.此方法通过调整个体学
习器的集成权重来匹配用户需求,而非重新训练个体学习器,因此降低了计算资源消耗. |
| 英文摘要 |
| Online diverse content generation is one of the emergent research directions in the field of procedural content
generation in recent years. It can not only meet users’ different preferences and enhance user experience, but also provide a
large amount of scenarios and problems for training and testing artificial intelligence algorithms. Recent research proposed
online diverse content generation methods based on negatively correlated ensemble reinforcement learning, such methods
can not effectively meet the specific preferences of different users. Furthermore, training and deploying individual learning
models requires significant computational resources. To address those two issues, this paper proposes an online content
generation approach based on multi-objective ensemble pruning, built upon the negatively correlated ensemble reinforce
ment learning framework. This approach searches for the weights for integrating individual learning models through an
efficient multi-objective optimization algorithm, so that the obtained ensemble model can not only effectively match user
preferences, but also offer a Pareto set that exhibits a tradeoff between model performance and computational resource
consumption. This approach matches user preferences by adjusting the weights of individual learning models instead of
retraining models, thereby reducing the computational resource consumption. |
|
|
|
|
|