The Role of Machine Learning in Game Development Domain - A Review of Current Trends and Future Directions

被引:2
|
作者
Edwards, Gemma [1 ]
Subianto, Nicholas [1 ]
Englund, David [1 ]
Goh, Jun Wei [1 ]
Coughran, Nathan [1 ]
Milton, Zachary [1 ]
Mirnateghi, Nima [1 ]
Shah, Syed Afaq Ali [1 ,2 ]
机构
[1] Murdoch Univ, Discipline Informat Technol, Murdoch, WA, Australia
[2] Edith Cowan Univ, Sch Sci, Perth, WA, Australia
来源
2021 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2021) | 2021年
关键词
Machine Learning; Artificial Intelligence; Video Games; Adaptive NPCs; Game Development; ARTIFICIAL-INTELLIGENCE; AI; FRAMEWORK; AGENTS;
D O I
10.1109/DICTA52665.2021.9647261
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Machine Learning is a relatively new and emergent field in video game development domain. Despite considerable relevance to the video game industry, there has yet to be a significant commercial product utilising machine learning in its design or function. Although previous research has shown significant potential in the use of video games in developing and testing Artificial Intelligence, the reverse i.e., using Artificial Intelligence to develop and test video games is far less common. This paper provides a survey of existing techniques and reviews current and future applications of machine learning in the field of video game development, both as a tool to streamline development and management processes and as an integrated part of video game end products themselves. This paper also explores a number of machine learning technologies not yet applied to use in the video game field and discusses their potential in future research and product development. Despite the relative newness and lack of development in this field, this paper finds that there is potential for machine learning to significantly improve and expedite production in the video game industry. Machine Learning can potentially be harnessed to develop new or improved products or automate development processes in conventional video game products.
引用
收藏
页码:495 / 501
页数:7
相关论文
共 50 条
  • [41] Applications of AI and machine learning in mining: digitization and future directions
    Arun Kumar Sahoo
    Debi Prasad Tripathy
    Safety in Extreme Environments, 2025, 7 (1):
  • [42] Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions
    J. Matthew Helm
    Andrew M. Swiergosz
    Heather S. Haeberle
    Jaret M. Karnuta
    Jonathan L. Schaffer
    Viktor E. Krebs
    Andrew I. Spitzer
    Prem N. Ramkumar
    Current Reviews in Musculoskeletal Medicine, 2020, 13 : 69 - 76
  • [43] Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions
    Helm, J. Matthew
    Swiergosz, Andrew M.
    Haeberle, Heather S.
    Karnuta, Jaret M.
    Schaffer, Jonathan L.
    Krebs, Viktor E.
    Spitzer, Andrew, I
    Ramkumar, Prem N.
    CURRENT REVIEWS IN MUSCULOSKELETAL MEDICINE, 2020, 13 (01) : 69 - 76
  • [44] Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions
    Shujaat, Sohaib
    DIAGNOSTICS, 2025, 15 (03)
  • [45] Integrating Artificial Intelligence in Stroke Rehabilitation: Current Trends and Future Directions; A mini review
    Afridi, Ayesha
    Obaid, Sumaiyah
    Raheel, Neha
    Rathore, Farooq Azam
    JOURNAL OF THE PAKISTAN MEDICAL ASSOCIATION, 2025, 75 (02) : 339 - 341
  • [46] Emerging technologies for efficient water use in agriculture: A review of current trends and future directions
    Antu, Uttam Biswas
    Islam, Md. Saiful
    Ahmed, Sujat
    Arifuzzaman, Md.
    Saha, Sawmitra
    Mitu, Puja Rani
    Sarkar, Aditya Raj
    Mahiddin, Nor Aida
    Ismail, Zulhilmi
    Ibrahim, Khalid A.
    Idris, Abubakr M.
    JOURNAL OF WATER PROCESS ENGINEERING, 2024, 68
  • [47] Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions)
    Hasani, Navid
    Paravastu, Sriram S.
    Farhadi, Faraz
    Yousefirizi, Fereshteh
    Morris, Michael A.
    Rahmim, Arman
    Roschewski, Mark
    Summers, Ronald M.
    Saboury, Babak
    PET CLINICS, 2022, 17 (01) : 145 - 174
  • [48] Reinforcement learning in sentiment analysis: a review and future directions
    Eyu, Jer Min
    Yau, Kok-Lim Alvin
    Liu, Lei
    Chong, Yung-Wey
    ARTIFICIAL INTELLIGENCE REVIEW, 2024, 58 (01)
  • [49] Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions
    Song, Binyang
    Zhou, Rui
    Ahmed, Faez
    JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2024, 24 (01)
  • [50] Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions
    Salcedo, Dixon
    Guerrero, Cesar
    Saeed, Khalid
    Mardini, Johan
    Calderon-Benavides, Liliana
    Henriquez, Carlos
    Mendoza, Andres
    ELECTRONICS, 2022, 11 (23)