Stories of the town: Balancing character autonomy and coherent narrative in procedurally generated worlds
Procedural narrative generation systems often focus on autonomous agent based simulations to create emergent interactions, plan-based approaches to provide guarantees for coherence, or using elements of simulation to guide plan-based approaches. These different approaches, with some exceptions, tend to trade off character autonomy in service of more designer controlled experiences or content authoring in service of encoding domain knowledge of possible branches of the narrative and participating characters. We have developed a system, called Stories of the Town, that automatically generates narratives by synthesizing three distinct approaches to traditional narrative generation: context-free grammars, planning, and simulation. More specifically, our system generates narratives via probabilistic context-free grammars applied to state-space planning problem solutions from planning problem formulations of simulated character models. Our system uses character simulations to generate variety in narratives and ensures narrative coherence through authoring probabilistic context-free grammars. By doing so, this system takes advantage of the strengths of each individual approach (e.g. controllability, scalability, intentionality, and variety) to generate narratives that are extensible, expressive, consistent with simulated character personalities and histories, and controllable. We show that this system has strong potential in automatically generating varied, complex, consistent, and goal-oriented narratives. Further development of the system will allow for more efficient utilization of the strengths of each narrative generation approach while also using these strengths to supplement their individual shortcomings.
