Evaluating the Authorial Leverage of Drama Management

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