The plot (or narrative) of a story has some similarity with a plan, as it is usually defined in classical AI planning. This has led researchers to investigate planning-based approaches to automatic story generation (see, for example, the work of Porteous et al. and their demo). However, the planning approach requires as input a formal description of the "world" in which the story is to take place - it's characters, places, objects, their relations, and the things they can do. Writing this world model is a great burden on system designers, and limits the creativity that story generation systems can exhibit. On the other hand, machine learning approaches can produce generative models from data in a mostly unsupervised manner, but these models so far are only able to replicate the surface appearance of fiction and fail to generate a coherent plot (sunspring is an excellent example in point).
The ultimate goal of automated story generation is to release unbounded creativity while still making sense. As one way to approach this problem, we can look to the already large, and growing, collections of structured semantic knowledge that is available on the web. Initiatives such as conceptnet and read the web offer large databases of facts and relations (both abstract and concrete) obtained from text mining and crowdsourcing, while ontologies can provide knowledge about specific areas, or general things like time (see, for example, vocab.org).
Students working on this project need to have a background in computer science and good programming skills.
- Follow the links!
- Haslum, "Narrative Planning: Compilations to Classical Planning", Journal of AI Research, vol. 44, p. 383-395, 2012, also available on-line from JAIR.
- Julie Porteous, Fred Charles and Marc Cavazza, "NetworkING: using Character Relationships for Interactive Narrative Generation", International Conference on Autonomous Agents and Multiagent Systems, 2013 (available here).
Previous students' work on this topic:
- David Cowley carried out a user study in interactive fiction and player modelling (honours thesis, 2015).
- Emily Rodrigo implemented a Markov chain model for text generation, and explored the question if coupling it with a topic model could generate text that was more coherent and sensible (report, 2017).
- William James (co-supervised with Hanna Suominen) implemented the beginnings of an NLP pipeline for extracting action/event models from text (honours thesis, 2017).
- Musha Wen investigated the use of the conceptnet database, combined with NLP extraction techniques, to find event relations that may be used to create story planning operators (report, 2018).
- Louis Carlin implemented and evaluated another technique for extracting event models from text (report, 2019).
- Jiaqi Zhang (report, 2019) and Debashish Chakraborty (thesis, 2019) both studied the shortcomings of GAN-based text generation.