Authorial Leverage: Artificial Intelligence for Narrative and Storytelling
Intelligent Narrative Technologies have changed storytelling by facilitating new types of story experiences, such as interactive stories. However, Intelligent Narrative Technologies also introduce a prohibitively high cost of authorship, which goes beyond the effort it takes to learn how to use new technology and tools. New technology replaces aspects of traditional human authoring with instructions and procedures. Evaluating the power of expressiveness in different storytelling approaches is similar to understanding the difference in utility between a hammer and a nail gun, or between Python and Assembly.
This dissertation asks why such authorship is so difficult, and proposes a technique for making it easier. The three main contributions of this work are: (1) it provides an author-centric design model for intelligent narrative, positioning storytelling as the management of variations; (2) it introduces the Authorial Leverage (AL) framework, a means for evaluating the costs and benefits of interactive story authorship; and (3) it introduces a system designed to demonstrate the aforementioned concepts.
The author-centric design model sees interactive stories as composed of constituent and supplementary events, as opposed to the traditional story-and- discourse model. This allows us to build interactive storytelling tools with greater authorial leverage. We describe one such system, called RoleModel.