User-driven narrative variation in large story domains using monte carlo tree search
Bilal Kartal, John Koenig, Stephen J. Guy · 2014
Planning-based techniques are powerful tools for automated narrative generation, however, as the planning domain grows in the number of possible actions traditional planning tech-niques suffer from a combinatorial explosion. In this work, we apply Monte Carlo Tree Search to goal-driven narrative generation. We demonstrate our approach to have an or-der of magnitude improvement in performance over tradi-tional search techniques when planning over large story do-mains. Additionally, we propose a Bayesian story evaluation method to guide the planning towards believable narratives which achieve user-defined goals. Finally, we present an in-teractive user interface which enables users of our framework to modify the believability of different actions, resulting in greater narrative variety.