Development of Non-Character Player Using Self-Learning Algorithm for Artificial Intelligent Games

  • Authors

    • M A. Noryushan
    • N Zamin
    • H A. Rahim
    • M A. Sahari
    • N I. Hassan
    • Z M. Fauzee
    2018-05-16
    https://doi.org/10.14419/ijet.v7i2.28.12913
  • Artificial Intelligence, Self-learning Algorithms, Intelligent Agent, Non-Player Character.

  • Abstract

    In most of video games, the Non-Playing Character (NPC) behavior and movement are usually scripted. Players who have exploited the NPCs weaknesses will be able to beat them easily and there will be no freshness in player experiences. However, if the character can adapt and learn from the environment, it will be more interactive since players need to find new weaknesses to exploit. In this project, an agent that can learn by itself in the game which is introduced. This ongoing project investigates and compares the available self-learning algorithms used in game development and will be implemented as the intelligent agent. The Fourth Industrial Revolution (IR 4.0) has the potential to raise global income levels and improve the quality of life through Artificial Intelligence (AI) programs.  AI has made possible new products and services that increase the efficiency and pleasure of our personal lives such as dynamic games that can learn from its environment.

     

     

  • References

    1. [1] Motion Capture NYC. “How Much Does Motion Capture Cost?†motioncapturenyc.com. http://www.motioncapturenyc.com/ (accessed October 11th, 2017).

      [2] Stanley, Kenneth O., and Risto Miikkulainen. "Evolving Neural Networks Through Augmenting Topologies." Evolutionary computation 10, no. 2 (2002): 99-127.

      [3] Wen, Ruoshi, Zixi Guo, Tong Zhao, Xiang Ma, Qiang Wang, and Zhaojun Wu. "Neuroevolution of augmenting topologies based musculor-skeletal arm neurocontroller." In Instrumentation and Measurement Technology Conference (I2MTC), 2017 IEEE International, pp. 1 - 6. IEEE, 2017.

      [4] Marczyk, Adam. "Genetic Algorithms and Evolutionary Computation." The Talk Origins Archive: http://www. talkorigins/faqs/genalg/genalg.html (2004).

  • Downloads

  • How to Cite

    A. Noryushan, M., Zamin, N., A. Rahim, H., A. Sahari, M., I. Hassan, N., & M. Fauzee, Z. (2018). Development of Non-Character Player Using Self-Learning Algorithm for Artificial Intelligent Games. International Journal of Engineering & Technology, 7(2.28), 204-205. https://doi.org/10.14419/ijet.v7i2.28.12913

    Received date: 2018-05-16

    Accepted date: 2018-05-16

    Published date: 2018-05-16