IDEAS home Printed from https://ideas.repec.org/a/sae/simgam/v40y2009i6p752-801.html

Advances in Games Technology: Software, Models, and Intelligence

Author

Listed:
  • Edmond Prakash

    (Manchester Metropolitan University, Manchester, UK, e.prakash@mmu.ac.uk)

  • Geoff Brindle

    (Manchester Metropolitan University, Manchester, UK, g.brindle@mmu.ac.uk)

  • Kevin Jones

    (Nanyang Technological University, Singapore)

  • Suiping Zhou

    (Nanyang Technological University, Singapore)

  • Narendra S. Chaudhari

    (Nanyang Technological University, Singapore, asnarendra@ntu.edu.sg)

  • Kok-Wai Wong

    (Murdoch University, Murdoch, Western Australia, Australia)

Abstract

Games technology has undergone tremendous development. In this article, the authors report the rapid advancement that has been observed in the way games software is being developed, as well as in the development of games content using game engines. One area that has gained special attention is modeling the game environment such as terrain and buildings. This article presents the continuous level of detail terrain modeling techniques that can help generate and render realistic terrain in real time. Deployment of characters in the environment is increasingly common. This requires strategies to map scalable behavior characteristics for characters as well. The authors present two important aspects of crowd simulation: the realism of the crowd behavior and the computational overhead involved. A good simulation of crowd behavior requires delicate balance between these aspects. The focus in this article is on human behavior representation for crowd simulation. To enhance the player experience, the authors present the concept of player adaptive entertainment computing, which provides a personalized experience for each individual when interacting with the game. The current state of game development involves using very small percentage (typically 4% to 12%) of CPU time for game artificial intelligence (AI). Future game AI requires developing computational strategies that have little involvement of CPU for online play, while using CPU’s idle capacity when the game is not being played, thereby emphasizing the construction of complex game AI models offline. A framework of such nonconventional game AI models is introduced.

Suggested Citation

  • Edmond Prakash & Geoff Brindle & Kevin Jones & Suiping Zhou & Narendra S. Chaudhari & Kok-Wai Wong, 2009. "Advances in Games Technology: Software, Models, and Intelligence," Simulation & Gaming, , vol. 40(6), pages 752-801, December.
  • Handle: RePEc:sae:simgam:v:40:y:2009:i:6:p:752-801
    DOI: 10.1177/1046878109335120
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1046878109335120
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1046878109335120?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gary Klein, 1999. "Sources of Power: How People Make Decisions," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262611465, December.
    2. E. Bonabeau & M. Dorigo & G. Theraulaz, 2000. "Inspiration for optimization from social insect behaviour," Nature, Nature, vol. 406(6791), pages 39-42, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Steven C. Gold & Joseph Wolfe, 2012. "The Validity and Effectiveness of a Business Game Beta Test," Simulation & Gaming, , vol. 43(4), pages 481-505, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tim Rakow & Charles Vincent & Kate Bull & Nigel Harvey, 2005. "Assessing the Likelihood of an Important Clinical Outcome: New Insights from a Comparison of Clinical and Actuarial Judgment," Medical Decision Making, , vol. 25(3), pages 262-282, May.
    2. Dong, Yingchao & Zhang, Shaohua & Zhang, Hongli & Zhou, Xiaojun & Jiang, Jiading, 2025. "Chaotic evolution optimization: A novel metaheuristic algorithm inspired by chaotic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 192(C).
    3. Jordan Vazquez & Cécile Godé & Jean-Fabrice Lebraty, 2018. "Environnement big data et décision : l'étape de contre la montre du tour de France 2017," Post-Print halshs-02188793, HAL.
    4. David Williams, 2014. "Models, Metaphors and Symbols for Information and Knowledge Systems," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 10(1), pages 79-107.
    5. Kim, Jong Hyun & Seong, Poong Hyun, 2007. "The effect of information types on diagnostic strategies in the information aid," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 171-186.
    6. Liying Xu & Jiadi Zhu & Bing Chen & Zhen Yang & Keqin Liu & Bingjie Dang & Teng Zhang & Yuchao Yang & Ru Huang, 2022. "A distributed nanocluster based multi-agent evolutionary network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Mike Metcalfe, 2013. "A Pragmatic System of Decision Criteria," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(1), pages 56-64, January.
    8. Betsch, Tilmann & Haberstroh, Susanne & Molter, Beate & Glockner, Andreas, 2004. "Oops, I did it again--relapse errors in routinized decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 93(1), pages 62-74, January.
    9. Lorko, Matej & Servátka, Maroš & Zhang, Le, 2019. "How to Improve the Accuracy of Project Schedules? The Effect of Project Specification and Historical Information on Duration Estimates," MPRA Paper 95585, University Library of Munich, Germany.
    10. Xiaoqing Zhao & Qifa Yue & Jianchao Pei & Junwei Pu & Pei Huang & Qian Wang, 2021. "Ecological Security Pattern Construction in Karst Area Based on Ant Algorithm," IJERPH, MDPI, vol. 18(13), pages 1-21, June.
    11. Jan Hayes & Sarah Maslen, 2015. "Knowing stories that matter: learning for effective safety decision-making," Journal of Risk Research, Taylor & Francis Journals, vol. 18(6), pages 714-726, June.
    12. Jordan Vazquez & Cécile Godé & Jean-Fabrice Lebraty, 2019. "Environnement big data et prise de décision intuitive : le cas de la Police Nationale des Bouches du Rhône," Post-Print halshs-02188451, HAL.
    13. Peecher, Mark E. & Solomon, Ira & Trotman, Ken T., 2013. "An accountability framework for financial statement auditors and related research questions," Accounting, Organizations and Society, Elsevier, vol. 38(8), pages 596-620.
    14. Andersson, Patric & Engelberg, Elisabeth, 2006. "Affective and rational consumer choice modes: The role of intuition, analytical decision-making, and attitudes to money," SSE/EFI Working Paper Series in Business Administration 2006:13, Stockholm School of Economics.
    15. Gao, Shangce & Wang, Yirui & Cheng, Jiujun & Inazumi, Yasuhiro & Tang, Zheng, 2016. "Ant colony optimization with clustering for solving the dynamic location routing problem," Applied Mathematics and Computation, Elsevier, vol. 285(C), pages 149-173.
    16. Jordan Vazquez & Cécile Godé & Jean-Fabrice Lebraty, 2017. "Les enjeux des environnements big data pour la Police Nationale," Post-Print halshs-02188803, HAL.
    17. Clayton Wukich & Scott E. Robinson, 2013. "Leadership Strategies at the Meso Level of Emergency Management Networks," International Review of Public Administration, Taylor & Francis Journals, vol. 18(1), pages 41-59, April.
    18. Chiara Furio & Luciano Lamberti & Catalin I. Pruncu, 2024. "Mechanical and Civil Engineering Optimization with a Very Simple Hybrid Grey Wolf—JAYA Metaheuristic Optimizer," Mathematics, MDPI, vol. 12(22), pages 1-68, November.
    19. Giampiero E.G. Beroggi, 2003. "Internet Multiattribute Group Decision Support in Electronic Commerce," Group Decision and Negotiation, Springer, vol. 12(6), pages 481-499, November.
    20. Padraig MacNeela & Anne Scott & Pearl Treacy & Abbey Hyde, 2010. "In the know: cognitive and social factors in mental health nursing assessment," Journal of Clinical Nursing, John Wiley & Sons, vol. 19(9‐10), pages 1298-1306, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:simgam:v:40:y:2009:i:6:p:752-801. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.