What characters believe, how they act based on those beliefs,
and how their beliefs are updated is an essential element of
many stories. State-space narrative planning algorithms treat
their search spaces like a set of temporally possible worlds.
We present an extension that models character beliefs as epistemically possible worlds and describe how such a space is
generated. We also present the results of an experiment which
demonstrates that the model meets the expectations of a human audience.
Alireza Shirvani, Stephen G. Ware, Rachelyn Farrell. A possible worlds model of belief for state-space narrative planning. In Proceedings of the 13th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 101-107, 2017.