Evaluating the authorial leverage of drama management

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“Evaluating the authorial leverage of drama management” by Sherol Chen, Mark J. Nelson, and Michael Mateas. In Proceedings of the Fifth Artificial Intelligence and Interactive Digital Entertainment Conference, 2009, pp. 136-141.

Abstract

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.

BibTeX entry:

@inproceedings{Leverage:AIIDE09,
   author = {Sherol Chen and Mark J. Nelson and Michael Mateas},
   title = {Evaluating the authorial leverage of drama management},
   booktitle = {Proceedings of the Fifth Artificial Intelligence and
	Interactive Digital Entertainment Conference},
   pages = {136--141},
   year = {2009}
}

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