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A Simple Intensity-Based Drama Manager

  • Christopher Ramsley
  • Matthew Fugere
  • Randi Pawson
  • Charles Rich
  • Dean O’Donnell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6432)

Abstract

We have implemented an action-based role-playing game, called Wind’s End, that incorporates a simple, practical algorithm to enforce rising dramatic intensity during game play by controlling the choice of goals by nonplayer characters.

Keywords

story generation drama management demonstrations 

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References

  1. 1.
    Buckland, M.: Programming Game AI by Example. Wordware, Plano (2005)Google Scholar
  2. 2.
    Magerko, B.: Evaluating preemptive story direction in the interactive drama architecture. Journal of Game Development 2(3) (2006)Google Scholar
  3. 3.
    Nelson, M., Mateas, M.: Search-based drama management in the interactive fiction Anchorhead. In: Proc. 1st Int. Conf. on Artificial Intelligence and Interactive Digital Entertainment (2005)Google Scholar
  4. 4.
    Nelson, M., Roberts, D., Isbell, C., Mateas, M.: Reinforcement learning for declarative optimization-based drama management. In: Autonomous Agents and Multi-Agent Systems (2006)Google Scholar
  5. 5.
    Sharma, M., Santiago, O., Mehta, M., Ram, A.: Drama management and player modeling for interactive fiction games. Computational Intelligence 26(2), 183–211 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Young, R., Riedl, M., Branly, M., Jhala, A., Martin, R., Sagretto, C.: An architecture for integrating plan-based behavior generation with interactive game environments. Journal of Game Development 1(1) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christopher Ramsley
    • 1
  • Matthew Fugere
    • 1
  • Randi Pawson
    • 1
  • Charles Rich
    • 1
  • Dean O’Donnell
    • 1
  1. 1.Worcester Polytechnic InstituteWorcesterUSA

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