Towards a Resource-based Model of Strategy to Help Designing Opponent AI in RTS Games

Juliette Lemaitre, Domitile Lourdeaux, Caroline Chopinaud

2015

Abstract

The artificial intelligence used for opponent non-player characters in commercial real-time strategy games is often criticized by players. It is used to discover the game but soon becomes too easy and too predictable. Yet, a lot of research has been done on the subject, and successful complex behaviors have been created, but the systems used are too complicated to be used by the video games industry, as they would need time for the game designer to learn how they function, which ultimately proves prohibitive. Moreover these systems often lack control for the game designer to be adapted to the desired behavior. To address the issue, we propose an accessible strategy model that can adapt itself to the player and can be easily created and modified by the game designer.

References

  1. Bartle, R. (1996). Hearts, clubs, diamonds, spades: Players who suit MUDs. Journal of MUD research, 1(1):19.
  2. Bateman, C. and Boon, R. (2006). 21st Century Game Design. Charles river media edition.
  3. Buro, M. (2003). Real-time strategy games: A new AI research challenge. In IJCAI, pages 1534-1535.
  4. Churchill, D. and Buro, M. (2011). Build order optimization in StarCraft. In AIIDE.
  5. Dereszynski, E. W., Hostetler, J., Fern, A., Dietterich, T. G., Hoang, T.-T., and Udarbe, M. (2011). Learning probabilistic behavior models in real-time strategy games. In AIIDE.
  6. Lazzaro, N. (2004). Why we play games: Four keys to more emotion without story.
  7. Malone, T. W. (1980). What makes things fun to learn? heuristics for designing instructional computer games. In Proceedings of the 3rd ACM SIGSMALL symposium and the first SIGPC symposium on Small systems, pages 162-169. ACM.
  8. Nakamura, J. and Csikszentmihalyi, M. (2002). The concept of flow. In Handbook of positive psychology, pages 89-105.
  9. Ontanon, S., Mishra, K., Sugandh, N., and Ram, A. (2007). Case-based planning and execution for real-time strategy games. In Case-Based Reasoning Research and Development, pages 164-178. Springer.
  10. Ontanon, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., and Preuss, M. (2013). A survey of real-time strategy game AI research and competition in StarCraft. Computational Intelligence and AI in Games, IEEE Transactions on, 5(4):293-311.
  11. Orkin, J. (2004). Applying goal-oriented action planning to games. In AI Game Programming Wisdom.
  12. Palma, R., Gonzlez-Calero, P. A., Gmez-Martn, M. A., and Gmez-Martn, P. P. (2011). Extending case-based planning with behavior trees. In FLAIRS Conference.
  13. Read, J. C., MacFarlane, S. J., and Casey, C. (2002). Endurability, engagement and expectations: Measuring children's fun. In Interaction design and children, volume 2, pages 1-23. Shaker Publishing Eindhoven.
Download


Paper Citation


in Harvard Style

Lemaitre J., Lourdeaux D. and Chopinaud C. (2015). Towards a Resource-based Model of Strategy to Help Designing Opponent AI in RTS Games . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-073-4, pages 210-215. DOI: 10.5220/0005254402100215


in Bibtex Style

@conference{icaart15,
author={Juliette Lemaitre and Domitile Lourdeaux and Caroline Chopinaud},
title={Towards a Resource-based Model of Strategy to Help Designing Opponent AI in RTS Games},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2015},
pages={210-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005254402100215},
isbn={978-989-758-073-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Towards a Resource-based Model of Strategy to Help Designing Opponent AI in RTS Games
SN - 978-989-758-073-4
AU - Lemaitre J.
AU - Lourdeaux D.
AU - Chopinaud C.
PY - 2015
SP - 210
EP - 215
DO - 10.5220/0005254402100215