StoryFramer: From Input Stories to Output Planning Models

We are interested in the problem of creating narrative planning models for use in Interactive Multimedia Storytelling Systems. Modelling of planning domains has been identified as a major bottleneck in the wider field of planning technologies and this is particularly so for narrative applications where authors are likely to be non-technical. On the other hand there are many large corpora of stories and plot synopses, in natural language, which could be mined to extract content that could be used to build narrative domain models. In this paper we describe an approach to learning narrative planning domain models from input natural language plot synopses. Our approach, called StoryFramer, takes natural language input and uses NLP techniques to construct structured representations from which we build up domain model content. The system also prompts the user for input to disambiguate content and select from candidate actions and predicates. We fully describe the approach and illustrate it with an end-to-end worked example. We evaluate the performance of StoryFramer with NL input for narrative domains which demonstrate the potential of the approach for learning complete domain models.