ABSTRACT
A long-standing challenge in interactive entertainment is the creation of story-based games with dynamically responsive story-lines. Such games are populated by multiple objects and autonomous characters, and must provide a coherent story experience while giving the player freedom of action. To maintain coherence, the game author must provide for modifying the world in reaction to the player's actions, directing agents to act in particular ways (overriding or modulating their autonomy), or causing inanimate objects to reconfigure themselves "behind the player's back".Declarative optimization-based drama management is one mechanism for allowing the game author to specify a drama manager (DM) to coordinate these modifications, along with a story the DM should aim for. The premise is that the author can easily describe the salient properties of the story while leaving it to the DM to react to the player and direct agent actions. Although promising, early search-based approaches have been shown to scale poorly. Here, we improve upon the state of the art by using reinforcement learning and a novel training paradigm to build an adaptive DM that manages the tradeoff between exploration and story coherence. We present results on two games and compare our performance with other approaches.
- J. Bates. Virtual reality, art, and entertainment. Presence: The Journal of Teleoperators and Virtual Environments, 2(1):133--138, 1992. Google ScholarDigital Library
- A. Lamstein and M. Mateas. A search-based drama manager. In Proceedings of the AAAI-04 Workshop on Challenges in Game AI, 2004.Google Scholar
- B. Laurel. Toward the Design of a Computer-Based Interactive Fantasy System. PhD thesis, Drama department, Ohio State University, 1986.Google Scholar
- B. Magerko. Story representation and interactive drama. In Proceedings of the First Annual Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-05), 2005.Google ScholarDigital Library
- M. Mateas. An Oz-centric review of interactive drama and believable agents. In M. Woodridge and M. Veloso, editors, AI Today: Recent Trends and Developments. Lecture Notes in AI 1600. Springer, Berlin, NY, 1999. First appeared in 1997 as Technical Report CMU-CS-97-156, Computer Science Department, Carnegie Mellon University.Google ScholarDigital Library
- M. Mateas and A. Stern. Integrating plot, character, and natural language processing in the interactive drama Façade. In Proceedings of the 1st International Conference on Technologies for Interactive Digital Storytelling and Entertainment (TIDSE-03), 2003.Google Scholar
- M. J. Nelson and M. Mateas. Search-based drama management in the interactive fiction Anchorhead. In Proceedings of the First Annual Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-05), 2005.Google ScholarDigital Library
- J. B. Pollack and A. D. Blair. Why did TD-Gammon work? Advances in Neural Information Processing Systems, 9:10--16, 1997.Google Scholar
- R. S. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3:9--44, 1988. Google ScholarDigital Library
- G. Tesauro. Practical issues in temporal difference learning. Machine Learning, 8:257--277, 1992. Google ScholarDigital Library
- G. Tesauro. Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3):58--68, 1995. Google ScholarDigital Library
- P. Weyhrauch. Guiding Interactive Drama. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1997. Technical Report CMU-CS-97-109. Google ScholarDigital Library
- R. M. Young, M. O. Riedl, M. Branly, A. Jhala, R. J. Martin, and C. J. Saretto. An architecture for integrating plan-based behavior generation with interactive game environments. Journal of Game Development, 1(1), 2004.Google Scholar
Index Terms
- Reinforcement learning for declarative optimization-based drama management
Recommendations
Declarative Optimization-Based Drama Management in Interactive Fiction
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 ...
Evaluating a Drama Management Approach in an Interactive Fiction Game
WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02In this paper, we evaluate a drama management approach deployed in an implementation of a graphical interactive fiction game. Our approach uses players’ feedback as a basis for guiding the personalization of the interaction. Twenty subjects, with ...
Decomposing Drama Management in Educational Interactive Narrative: A Modular Reinforcement Learning Approach
Interactive StorytellingAbstractRecent years have seen growing interest in data-driven approaches to personalized interactive narrative generation and drama management. Reinforcement learning (RL) shows particular promise for training policies to dynamically shape interactive ...
Comments