AAAI Publications, Ninth Artificial Intelligence and Interactive Digital Entertainment Conference

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Generating Believable Stories in Large Domains
Bilal Kartal, John Koenig, Stephen J. Guy

Last modified: 2013-11-13


Planning-based techniques are a very powerful tool for automated story generation. However, as the number of possible actions increases, traditional planning techniques suffer from a combinatorial explosion due to large branching factors. In this work, we apply Monte Carlo Tree Search (MCTS) techniques to generate stories in domains with large numbers of possible actions (100+). Our approach employs a Bayesian story evaluation method to guide the planning towards believable stories that reach a user defined goal. We generate stories in a novel domain with different type of story goals. Our approach shows an order of magnitude improvement in performance over traditional search techniques.

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