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Issue No.03 - May/June (2006 vol.26)
pp: 32-41
Mark J. Nelson , Georgia Institute of Technology
Michael Mateas , Georgia Institute of Technology
David L. Roberts , Georgia Institute of Technology
Charles L. Isbell Jr. , Georgia Institute of Technology
ABSTRACT
A drama manager guides a player through a story experience by modifying the experience in reaction to the player's actions. Declarative optimization-based drama management (DODM) casts the drama-management problem as an optimization problem: the author declaratively specifies a set of plot points in a story, a set of actions the drama manager can take, and an evaluation function that rates a particular story. The drama manager then takes the actions in a way that attempts to maximize story quality. Peter Weyhrauch reported good results using a variant of game-tree search to optimize the use of drama-manager actions. The authors attempt to replicate these results on another story, Anchorhead, and show that search does not perform well in general, especially on larger and more complex stories. However, they believe that this is a problem with the specific optimization method, not the general approach, and report some results demonstrating the plausibility of applying reinforcement-learning techniques to compute a policy instead of search.
INDEX TERMS
interactive drama, computer gaming, drama management, game-tree search, reinforcement learning
CITATION
Mark J. Nelson, Michael Mateas, David L. Roberts, Charles L. Isbell Jr., "Declarative Optimization-Based Drama Management in Interactive Fiction", IEEE Computer Graphics and Applications, vol.26, no. 3, pp. 32-41, May/June 2006, doi:10.1109/MCG.2006.55
REFERENCES
1. M. Mateas and A. Stern, "Integrating Plot, Character, and Natural Language Processing in the Interactive Drama Façade," Proc. 1st Int'l Conf. Technologies for Interactive Digital Storytelling and Entertainment (TIDSE), Fraunhofer IRB Verlag, 2003.
2. P. Weyhrauch, Guiding Interactive Drama, doctoral dissertation, tech. report CMU-CS-97-109, School of Computer Science, Carnegie Mellon Univ., 1997.
3. R.S. Sutton, "Learning to Predict by the Methods of Temporal Differences," Machine Learning, vol. 3, no. 1, 1988, pp. 9–44.
4. G. Tesauro, "Practical Issues in Temporal Difference Learning," Machine Learning, vol. 8, nos. 3–4, 1992, pp. 257–277.
5. P. Dayan and T.J. Sejnowski, "TD(λ) Converges with Probability 1," Machine Learning, vol. 14, no. 3, 1994, pp. 295–301.
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