Evaluating the Authorial Leverage of Drama Management


  • Sherol Chen University of California, Santa Cruz
  • Mark Nelson Georgia Institute of Technology
  • Michael Mateas University of California, Santa Cruz




drama management, interactive narrative, evaluation


A drama manager (DM) monitors an interactive experience, such as a computer game, and intervenes to shape the global experience so that it satisfies the author’s expressive goals without decreasing a player’s interactive agency. Most research on drama management has proposed AI architectures and provided abstract evaluations of their effectiveness; a smaller body of work has also evaluated the effect of drama management on player experience. Little attention has been paid, however, to evaluating the authorial leverage provided by a drama-management architecture: determining, for a given architecture, the additional non-linear story complexity a drama manager affords over traditional scripting methods. In this paper, we propose three  criteria for evaluating the authorial leverage of a DM: (1) the script-and-trigger complexity of the DM story policy; (2) the degree of   policy change given changes to story elements; and (3) the average story branching factor for DM policies versus script-and-trigger policies for stories of equivalent quality. We apply these criteria to declarative optimization-based drama management (DODM) by using decision tree learning to capture equivalent trigger logic, and show that DODM does in fact provide authorial leverage




How to Cite

Chen, S., Nelson, M., & Mateas, M. (2009). Evaluating the Authorial Leverage of Drama Management. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 5(1), 136-141. https://doi.org/10.1609/aiide.v5i1.12377