Extending Narrative Planning Domains with Linguistic Resources

Interactive Narrative (IN) is an emerging application of planning for control of virtual character behaviours. Despite popularity in research, more widespread adoption of planning has been hindered by the difficulty of authoring planning domain models as IN's require models which are robust to dynamic environments and capable of generating a diversity of solutions. To reduce this authoring burden we explored automated extension of partially developed planning domains to address robustness and diversity. We introduce a novel algorithm, thype, which proposes additional types of characters and objects to extend a domain model. The extensions increase robustness by enlarging the range of ways to enact planned behaviours. In the paper we embed thype within a modular and extensible framework that allows multiple off-line generated extensions to be combined. We present results of a user study that show thype suggestions are plausible. We also empirically demonstrate the ability of thype extensions to increase the robustness of the models, demonstrate the modular nature of our extension framework by combining thype with another extension approach dedicated to recovery from plan failure and present results which show that enhanced performance results from combining multiple extensions.