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An Optimization Approach to Believable Behavior in Computer Games

  • Yifeng Zeng
  • Hua Mao
  • Fan Yang
  • Jian Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7607)

Abstract

Many artificial intelligence techniques have been developed to construct intelligent non-player characters (NPCs) in computer games. As games are gradually becoming an integral part of our life, they require human-like NPCs that shall exhibit believable behavior in the game-play. In this paper, we present an optimization approach to designing believable behavior models for NPCs. We quantify the notion of believability using a multi-objective function, and subsequently convert the achieving of believable behavior into one function optimization problem. We compute its analytical solutions and demonstrate the performance in a practical game.

Keywords

Action Node Game Designer Behavior Tree Believability Function Intelligent Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Amato, C., Shani, G.: High-level reinforcement learning in strategy games. In: Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent (AAMAS), pp. 75–82 (2010)Google Scholar
  2. 2.
    Bates, J.: Virtual reality, art and entertainment. Presence 1(1), 133–138 (1992)Google Scholar
  3. 3.
    Champandard, A.J.: Behavior trees for next-gen game ai. Tutorial, AiGameDev.com (2008)Google Scholar
  4. 4.
    Chang, Y., Maheswaran, R., Levinboim, T., Rajan, V.: Learning and evaluating human-like npc behaviors in dynamic games. In: Proceedings of the Seventh Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), pp. 8–13 (2011)Google Scholar
  5. 5.
    Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley-Interscience, New York (1991)zbMATHCrossRefGoogle Scholar
  6. 6.
    Doirado, E., Martinho, C.: I mean it!: detecting user intentions to create believable behaviour for virtual agents in games. In: Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent (AAMAS), pp. 83–90 (2010)Google Scholar
  7. 7.
    Isla, D.: Handling complexity in the halo 2 ai. In: Proceedings of the Fifteenth Conference on Game Developers Conference (2005)Google Scholar
  8. 8.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Statist. 22(1), 79–86 (1951)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: An architecture for general intelligence. Artificial Intelligence 33(1), 1–64 (1987)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Langley, P., Choi, D.: A unified cognitive architecture for physical agents. In: Proceedings of the Twenty-First AAAI Conference on Artificial Intelligence (AAAI), pp. 876–881 (2006)Google Scholar
  11. 11.
    Martinho, C., Paiva, A.: Using anticipation to create believable behaviour. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI), pp. 175–180 (2006)Google Scholar
  12. 12.
    Rabin, S.: AI Game Programming Wisdom 4. Course Technology (2009)Google Scholar
  13. 13.
    Scott Neal Reilly, W.: Believable Social and Emotional Agents. PhD thesis, School of Computer Science, Carnegie Mellon University (1996)Google Scholar
  14. 14.
    Riedl, M.O., Stern, A.: Believable agents and intelligent scenario direction for social and cultural leadership training. In: Proceedings of the Fifteenth Conference on Behavior Representation in Modeling and Simulation (2006)Google Scholar
  15. 15.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice-Hall (2003)Google Scholar
  16. 16.
    Tan, C.T., Cheng, H.: Implant: An integrated mdp and pomdp learning agent for adaptive games. In: Proceedings of the Fifth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), pp. 94–99 (2009)Google Scholar
  17. 17.
    Tence, F., Buche, C., De Loor, P., Marc, O.: The challenge of believability in video games: Definitions, agents models and imitation learning. CoRR abs/1009.0451 (2010)Google Scholar
  18. 18.
    Witten, I.H., Bell, T.C.: The zero-frequency problem: estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory 37(4), 1085–1094 (1991)CrossRefGoogle Scholar
  19. 19.
    Xu, J.Z., Laird, J.E.: Combining learned discrete and continuous action models. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI), pp. 1449–1454 (2011)Google Scholar
  20. 20.
    Zeng, Y., Buus, D.P., Hernandez, J.C.: Multiagent based construction for human-like architecture. In: Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2007), pp. 409–411 (2007)Google Scholar
  21. 21.
    Zeng, Y., Hernandez, J.C., Buus, D.P.: Swarmarchitect: a swarm framework for collaborative construction. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 186–186 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yifeng Zeng
    • 1
  • Hua Mao
    • 1
  • Fan Yang
    • 2
  • Jian Luo
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityDenmark
  2. 2.Department of AutomationXiamen UniversityChina

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