Decision-theoretic narrative planning for guided exploratory learning environments.

摘要

Interactive narrative environments have been the focus of increasing attention in recent years. A key challenge posed by these environments is narrative planning, in which a director agent orchestrates all of the events in an interactive virtual world. To create effective interactions, the director agent must cope with the task's inherent uncertainty, including uncertainty about the user's intentions. Moreover, director agents must be efficient so they can operate in real time. To address these issues, we present U-DIRECTOR, a decision-theoretic narrative planning architecture that dynamically models narrative objectives (e.g., plot progress, narrative flow), storyworld state (e.g., physical state, plot focus), and user state (e.g., goals, beliefs) with a dynamic decision network (DDN) that continually selects storyworld actions to maximize narrative utility on an ongoing basis. DDNs extend decision networks by introducing the ability to model attributes whose values change over time; decision networks extend Bayesian networks by supporting utility-based rational decision making. The U-DIRECTOR architecture also employs an n-gram goal recognition model that exploits knowledge of narrative structure to recognize users' goals and an HTN planner that operates in two coordinated planning spaces to integrate narrative and tutorial planning. U-DIRECTOR has been implemented in a narrative planner for an interactive narrative learning environment in the domain of microbiology in which a user plays the role of a medical detective solving a science mystery. Formal evaluations suggest that the U-DIRECTOR architecture satisfies the real-time constraints of interactive narrative environments and creates engaging experiences.