Last month I wrote a bit about text generation and generated narratives overall. This month, I’ve been looking more at parser games — games that typically are distinguished by (among other things) having an expressive (if not very discoverable) mode of input along with a complex world model.
My own first parser IF projects were very interested in that complexity. I liked the sensation of control that came from manipulating a detailed imaginary world, and the richness of describing it. And part of the promise of a complex world model (though not always realized in practice) was the idea that it might let players come up with their own solutions to problems, solutions that weren’t explicitly anticipated by the author.
It might seem like these are two extremes of the IF world: parser games are sometimes seen as niche and old-school, so much so that when I ran June’s London IF Meetup focused on Inform, we had some participants asking if I would start the session by introducing what parser IF is.
Meanwhile, generative text is sometimes not interactive at all. It is used for explorations that may seem high-concept, or else like they’re mostly of technical interest, in that they push on the boundaries of current text-related technology. (See also Andrew Plotkin’s project using machine learning to generate imaginary IF titles. Yes, as an intfiction poster suggested, that’s something you could also do with an older Markov implementation, but that particular exercise was an exercise in applying tech to this goal.)
There’s a tighter alignment between these types of project than might initially appear. Bruno Dias writes about using generative prose over on Sub-Q magazine. And Liza Daly has written about what a world model can do to make generated prose better, more coherent or more compelling.