@article{Nelson_Ashmore_Mateas_2021, title={Authoring an Interactive Narrative with Declarative Optimization-Based Drama Management}, volume={2}, url={https://ojs.aaai.org/index.php/AIIDE/article/view/18761}, DOI={10.1609/aiide.v2i1.18761}, abstractNote={<p>Drama managers reconfigure a game in reaction to a player’s actions. In declarative optimization-based drama management (DODM), a game’s story is abstracted as a sequence of plot points; possible drama manager interventions are abstracted as a set of DM actions. The author defines an func- tion evaluating story quality, and some optimization method (currently reinforcement learning) chooses DM actions so as to maximize expected story quality according to that evaluation function. While previous work has developed this ap- proach at a technical level and discussed its potential applications, no work to date has used DODM to write real games. We report on our experiences designing a game in the Neverwinter Nights engine, entitled The Guilty, in which we use DODM to create a dynamic plot that in a previous design iteration we had found difficult to create with other techniques.</p>}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}, author={Nelson, Mark and Ashmore, Calvin and Mateas, Michael}, year={2021}, month={Sep.}, pages={127-129} }