Narrative generation: balancing plot and character

• Authors: Mark Owen Riedl;R. Michael Young
• Venue: Narrative generation: balancing plot and character
• Year: 2004

The ability to generate narrative is of importance to computer systems that wish to use story effectively for a wide range of contexts ranging from entertainment to training and education. The typical approach for incorporating narrative into a computer system is for system builders to script the narrative features at design time. A central limitation of this pre-scripting approach is its lack of flexibility—such systems cannot adapt the story to the user's interests, preferences, or abilities. The alternative approach is for the computer systems themselves to generate narrative that is fully adapted to the user at run time. A central challenge for systems that generate their own narrative elements is to create narratives that are readily understood as such by their users. I define two properties of narrative—plot coherence and character believability—which play a role in the success of a narrative in terms of the ability of the narrative's audience to comprehend its structure. Plot coherence is the perception by the audience that the main events of a story have meaning and relevance to the outcome of the story. Character believability is the perception by the audience that the actions performed by characters are motivated by their beliefs, desires, and traits. In this dissertation, I explore the use of search-based planning as a technique for generating stories that demonstrate both strong plot coherence and strong character believability. To that end, the dissertation makes three central contributions. First, I describe an extension to search-based planning that reasons about character intentions by identifying possible character goals that explain their actions in a plan and creates plan structure that explains why those characters commit to their goals. Second, I describe how a character personality model can be incorporated into planning in a way that guides the planner to choose consistent character behavior without strictly preventing characters from acting “out of character” when necessary. Finally, I present an open-world planning algorithm that extends the capabilities of conventional planning algorithms in order to support a process of story creation modeled after the process of dramatic authoring used by human authors. This open-world planning approach enables a story planner not only to search for a sequence of character actions to achieve a set of goals, but also to search for a possible world in which the story can effectively be set. The planning algorithms presented in this dissertation are used within a narrative generation system called Fabulist. Fabulist generates a story as a sequence of character actions and then recounts the story by first generating a discourse plan that specifies how the story content should be told and then realizing the discourse plan in a storytelling medium. I present the results of an empirical evaluation that demonstrates that narratives generated by Fabulist have strong plot coherence and strong character believability. The results clearly indicate how a planning approach to narrative generation that reasons about plot coherence and character believability can improve the audience's comprehension of plot and character.