相关实验视频
Updated: Feb 26, 2026

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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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一个数据集的游戏视频与框架智能板注释,块注释,和状态映射连接四游戏的状态映射
Pankaj Kumar G1,2, Anitha M L3, Arun Kumar M N2
1PET Research Centre, PES College of Engineering, Visvesvaraya Technological University, Belagavi 590018, India.
Data in brief
|February 25, 2026
概括
本研究介绍了来自现实世界游戏中的Connect Four数据集,使视觉感知和状态表示成为可能. 它有助于计算机视觉任务,如板状态提取和视觉到符号映射.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 游戏人工智能 游戏AI
背景情况:
- 开发用于物理游戏分析的数据集对于推进人工智能至关重要.
- 现实世界的数据捕捉了在模拟环境中经常错过的细微差别.
- 结构化的状态表示是游戏中AI决策的关键.
研究的目的:
- 创建一个物理连接四个游戏的全面数据集.
- 为了使研究视觉感知和结构化状态表示的棋盘游戏.
- 为视觉到符号映射和AI状态动作建模提供资源.
主要方法:
- 记录人对人连接 物理板上有四个匹配.
- 标注板区域和个别片段位置的视频.
- 开发一个注释程序来生成结构板矩阵和向量化状态.
- 通过计算生成一百万个独特董事会职位的辅助数据集.
主要成果:
- 一个包含52个注释游戏的数据集,包含原始视频,注释区域,块位置和向量化状态.
- 一个辅助数据集,包含100万个具有移动标签的独特可玩的棋盘位置.
- 从现实世界的录音中提取视觉和象征性游戏状态的可行性.
结论:
- 该数据集为训练AI模型进行视觉游戏状态识别提供了有价值的资源.
- 它支持研究,弥合视觉输入和象征性游戏表示之间的差距.
- 该数据集促进了国家行动学习和游戏AI开发的下游应用.
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