Learning-Based Video Game Development in MLP@UoM: An Overview

被引:0
|
作者
Chen, Ke [1 ]
机构
[1] Univ Manchester, Dept Comp Sci, Manchester M13 9PL, Lancs, England
关键词
video game development; machine learning; procedural content generation; serious education games; fast skill capture; learnable agent;
D O I
10.1109/iceeie47180.2019.8981430
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In general, video games not only prevail in entertainment but also have become an alternative methodology for knowledge learning, skill acquisition and assistance for medical treatment as well as health care in education, vocational/military training and medicine. On the other hand, video games also provide an ideal test bed for AI researches. To a large extent, however, video game development is still a laborious yet costly process, and there are many technical challenges ranging from game generation to intelligent agent creation. Unlike traditional methodologies, in Machine Learning and Perception Lab at the University of Manchester (MLP@UoM), we advocate applying machine learning to different tasks in video game development to address several challenges systematically. In this paper, we overview the main progress made in MLP@UoM recently and have an outlook on the future research directions in learning-based video game development arising from our works.
引用
收藏
页码:358 / 363
页数:6
相关论文
共 50 条
  • [41] Trade Privacy for Utility: A Learning-Based Privacy Pricing Game in Federated Learning
    Wang, Yuntao
    Su, Zhou
    Pan, Yanghe
    Benslimane, Abderrahim
    Liu, Yiliang
    Luan, Tom H.
    Li, Ruidong
    ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 6307 - 6311
  • [42] Development and assessment of a chemistry-based video game
    Weaver, Gabriela C.
    Morales, Carlos
    Martinez-Hernandez, Kermin Joel
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2007, 233 : 232 - 232
  • [43] An Overview of Open Source Deep Learning-Based Libraries for Neuroscience
    Tshimanga, Louis Fabrice
    Del Pup, Federico
    Corbetta, Maurizio
    Atzori, Manfredo
    APPLIED SCIENCES-BASEL, 2023, 13 (09):
  • [44] The Research Overview of Manifold learning-based Dimension Reduction Technology
    Zhao Ying-gang
    Gong Lei
    THIRD INTERNATIONAL SYMPOSIUM ON ELECTRONIC COMMERCE AND SECURITY WORKSHOPS (ISECS 2010), 2010, : 260 - 263
  • [45] Deep learning-based smoker classification and detection: An overview and evaluation
    Khan, Ali
    Elhassan, Mohammed A. M.
    Khan, Somaiya
    Deng, Hai
    EXPERT SYSTEMS WITH APPLICATIONS, 2025, 267
  • [46] Umami-MRNN: Deep learning-based prediction of umami peptide using RNN and MLP
    Qi, Lulu
    Du, Jialuo
    Sun, Yue
    Xiong, Yongzhao
    Zhao, Xinyao
    Pan, Daodong
    Zhi, Yueru
    Dang, Yali
    Gao, Xinchang
    FOOD CHEMISTRY, 2023, 405
  • [47] A novel deep learning-based approach for video quality enhancement
    Moghaddam, Parham Zilouchian
    Modarressi, Mehdi
    Sadeghi, Mohammad Amin
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 144
  • [48] Prioritizing test cases for deep learning-based video classifiers
    Li, Yinghua
    Dang, Xueqi
    Ma, Lei
    Klein, Jacques
    Bissyande, Tegawende F.
    EMPIRICAL SOFTWARE ENGINEERING, 2024, 29 (05)
  • [49] Learning-based Caching with Unknown Popularity in Wireless Video Networks
    Tan, Yuanyuan
    Yuan, Yiling
    Yang, Tao
    Hu, Bo
    2017 IEEE 85TH VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING), 2017,
  • [50] Contextual Bandit Learning-Based Viewport Prediction for 360 Video
    Heyse, Joris
    Vega, Maria Torres
    De Backere, Femke
    De Turck, Filip
    2019 26TH IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES (VR), 2019, : 972 - 973