Director Agent Intervention Strategies for Interactive Narrative Environments

摘要

Interactive narrative environments offer significant potential for creating engaging narrative experiences. Increasingly, applications in education, training, and entertainment are leveraging narrative to create rich interactive experiences in virtual storyworlds. A key challenge posed by these environments is building an effective model of the intervention strategies of director agents that craft customized story experiences for users. Identifying factors that contribute to determining when the next director agent decision should occur is critically important in optimizing narrative experiences. In this work, a dynamic Bayesian network framework was designed to model director agent intervention strategies. To create empirically informed models of director agent intervention decisions, we conducted a Wizard-of-Oz (WOZ) data collection with an interactive narrative-centered learning environment. Using the collected data, dynamic Bayesian network and naive Bayes models were learned and compared. The performance of the resulting models was evaluated with respect to classification accuracy and produced promising results.