Curating Simulated Storyworlds (James Ryan) – Ch 6f

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This is the third of several posts about James Ryan’s dissertation, Curating Simulated Storyworlds. We are now reading chapters 6 and following, in which Ryan describes his own projects in the curated emergent narrative space.

After the first five chapters, this piece becomes considerably more narrative in its own structure: Ryan is (consciously) telling the story of his own artistic development and practice, and the particular works to which it gave rise.

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Curating Simulated Storyworlds (James Ryan) – Ch 4-5

This is the second of several posts about James Ryan’s dissertation, Curating Simulated Storyworlds. The previous post looked at chapters 1-3, which set out the concept of the dissertation and documented the pleasures of emergent narrative.

Here I read Chapter 4, concerned with the pain of emergent narrative, including critiques from other scholars and projects in emergent narrative that have failed; and Chapter 5, in which he presents his argument for curationist emergent narrative.

The major issues Ryan identifies with simulations are:

Boringness. Some simulations are simulating events that aren’t that engaging, and therefore they will never have the range to compel readers. (Something I was wondering about while reading chapters 1-3.)

Granularity extremes. The system is operating on either too large or too small a scale. As an example, Ryan showcases the system that controls how drinks may be taken in the Saga II story generation system, with an arguably excessive focus on moving objects from hand to hand.

  • As a side note: this is a granularity of state that most text adventure games wouldn’t bother with. There are some exceptions, though a few of the most granular works I know of were also never finished: for many years NK Guy worked on a game code-named Hamsterworld, which attended to player clothes and body parts (and many other systems) with great precision; of Gunther Schmidl’s And the Waves Choke the Wind, only a first few scenes were ever released. TADS 3’s library supports more in this range than any other text adventure world model I’m aware of, and handles some of the related challenges around making small actions implicit when they aren’t individually very interesting, so that at its best, the granularity of the world model becomes invisible except when there is something down in those details that really does interfere in the player’s intended action, at which point the consequence is reported. Return to Ditch Day remains one of the best examples of this kind of work, and Eric Eve’s work is also exemplary here.

Lack of modularity. The idea here is that elements of the simulation must be small and reusable; otherwise, it isn’t possible to recombine them in interesting ways. To illustrate this issue, Ryan looks at Sheldon Klein’s murder mystery generator, an example I haven’t seen written up particularly often (though perhaps I’ve been looking in the wrong places).

Lack of abstraction. Here, Ryan argues for the value of simulators that can cast different characters in different spaces and situations, rather than retelling (possibly different) stories about the same set of characters and events, since if we have a large number of stories about different characters, the appeal of the vast and the appeal of the ephemeral are preserved. (These are key features of the aesthetic of emergent narrative, as Ryan lays these out in earlier chapters.)

I am not sure what I think about this one. I will grant that the repetition of the same characters can give a kind of sameyness to story generators — though some systems, from Fallen London to Rafael Pérez y Pérez‘ Mexica, refer to characters by title or function in order to avoid the concrete effect of granting them a name.

Modeling gaps. This refers to places where it seems the simulation ought to cover some possibility or set of actions, based on what else is modeled, but for some reason certain elements are omitted.

Causality issues. Here Ryan describes how simulation causality can be too diffuse to make for good storytelling, especially in systems that rely on utility scoring where many different aspects of world state could all be considered to partially explain a particular outcome. (He gives a detailed example based on trying to interpret consequence in Prom Week, which is especially valuable here.) Though I’ve encountered this phenomenon, I haven’t seen the problem labeled or analyzed in depth before.

The solution Ryan proposes — contingent unlocking, where some events explicitly are made possibly by a finite set of prior conditions, and causal bookkeeping, where the system somewhere records how a particular outcome has been made available — will apparently come back in later chapters when he talks about his own work.

It’s a method we also used to some degree in Versu, where characters could record a string that represented why they’d adopted a particular attitude towards the player; and for that matter I use it lightly in my Choice of Games work in progress, which is not a simulation of the kind Ryan is talking about at all, but I still find it useful for the sake of later callbacks to be able to recall, say, the worst thing one character has ever done to another.


After these, Ryan next identifies pains of curation, and this is where the gloves come off.

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Curating Simulated Storyworlds (James Ryan) – Ch 1-3

James Ryan recently graduated from UC Santa Cruz, and he was kind enough to make available his dissertation, Curating Simulated Storyworlds, for anyone to read. Of academic work coming out recently, this is one of the more interesting to the interactive fiction crowd, and I’ve already recommended it to quite a few people. I’m going to be writing about it in a few posts, since it’s long enough that I wasn’t able to read it in a single sitting.

As with other posts about academic work, I’m aiming partly to make interesting academic work on interactive narrative visible and accessible to hobbyists and people from the game industry; but I also use the opportunity to record my own thoughts and reactions to the material, and these are often based especially on the history of interactive fiction. So while Ryan’s dissertation is not primarily about text adventures, I will sometimes draw connections from his ideas to work from the text adventure community.

The basic idea: Ryan is interested in the kinds of emergent stories that can be built by Dwarf Fortress-like simulations — large, complex worlds that generate many many events over many simulated years of interaction, often with striking and memorable chains of causality. But from a narrative perspective, experiencing these worlds is not always satisfying. Sometimes they generate fascinating emergent plots. Sometimes they just seem unfocused or dull. Hence: curation. We need either a human being or a second AI system capable of extracting the good stories from the simulator and presenting those to the reader:

To understand the successes, we might ask this essential question: what is the pleasure of emergent narrative? I contend that the form works more like nonfiction than fiction—emergent stories actually happen—and this produces a peculiar aesthetics that undergirds the appeal of its successful works. What then is the pain of emergent narrative? There is a ubiquitous tendency to misconstrue the raw transpiring of a simulation (or a trace of that unfolding) as being a narrative artifact, but such material will almost always lack story structure. (xii)

This is an area that a few others have touched on; Jacob Garbe’s Dwarf Grandpa project is essentially about curating a simulated storyworld.

In essence, Ryan’s assertion at the beginning of the dissertation appears to be that the difference between good and bad emergent narrative generators is simply whether anyone is sufficiently interested to bother curating the output: so Dwarf Fortress and the Sims are good emergent narrative generators because people retell their constructs, while some academic projects are not because no one is moved to retell those. To me this did seem to miss some points about what makes generators effective, including

  • whether they use a number of systems that interlock in interesting ways (this is a somewhat handwavy description, but Tarn Adams describes the point much more effectively)
  • whether the systems account for the possibility of stakes and motivations, or whether they mostly model less interesting things
  • whether the components of the systems are polysemous or symbolically rich, thus capable of supporting additional interpretive constructions beyond what the author might have intended
  • what range of outcomes and story shapes can be achieved; the expressive range of the generator

…though it may be that Ryan will come back to those or similar points later in the dissertation.

Ryan’s approach includes an explicit, extensive discussion of the aesthetics of emergent narrative. Why are we even bothering with this, and what experiences are we attempting to achieve? What does emergent narrative make possible, and what are the problems with it?

I was very glad to see this, because I think this kind of discussion is of critical interest for people who approach these systems from an artistic perspective, and they’re often entirely omitted or at best not very thoroughly considered in academic writing on procedural narrative systems.

The dissertation is sizable, so I’m going to be talking about it in a multiple chunks here.

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Choice Poetics (Peter Mawhorter)

Peter Mawhorter is an academic who looks at how choices work in interactive narrative, elaborating a theory of choice poetics. His articles offer some taxonomies and vocabulary for talking about choice design — with partial, not complete, overlap with IF community terminology for these topics — and he has built a system that procedurally generates new choices from scratch.

In this post, I’m looking at three of his articles and offering some thoughts of my own, but all three are linked and accessible without a paywall, so if you find this interesting you can read the originals. This is part of a series in which I’m looking at academic approaches to interactive fiction and related topics.

Towards a Theory of Choice Poetics (Peter Mawhorter et al) sets the stage for later work and argues that there is a field here worth looking at. As the title would suggest (“Towards…”), he’s not advancing a completed theory himself here, but pointing out some of the factors that would go into such a theory. The article is thus mostly a set of annotated lists: of player motives in choosing options in a game; of play styles; of choice structure styles, as defined by the outcomes of the choice; and “dimensions of player experience”, which I found at once the most interesting and most slippery of his groupings.

He is careful always to point out that these category lists aren’t, and don’t expect to be, complete.

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Survey of Storylets-based Design

Sketching a Map of the Storylets Design Space” is a paper by Max Kreminski prepared for ICIDS 2018, an academic survey of the storylet design space. I wanted to point my blog readers towards it, as it covers a lot of interesting territory in the quality-based narrative/salience-based narrative area (and in fact references my post on structures beyond branching narrative). Those of you interested in ways of structuring IF with more procedural complexity than a branching narrative may find it interesting.

The paper offers an overview of major storylet-based tools and works from both indie and academic experiments new and old, including StoryAssembler, StoryNexus, The King of Chicago, Reigns, and Ice-bound Compendium. It does not discuss Varytale, but then Varytale has been unavailable to play with for some years now, so there may not have been an accessible version for Kreminiski to look at since beginning their research.

Kreminski identifies four dimensions for looking at storylet-based systems: how preconditions for storylets are defined; whether individual storylets can ever be repeated; what sort of content is contained within a storylet (linear text? replacement grammars? branching content?); and finally, the “content selection architecture”, or how storylets are chosen as eligible for display.

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Kreminski also built a storylet system and a small prototype game of their own, a piece called Starfreighter. (It’s available on itch, if you want to look at it yourself.) The scenario is a fairly standard space-trading story, in which you accumulate crew and cargo and travel through a procedurally generated graph of solar systems. There are several cool presentational aspects here, including the way that you can select place names in your storylet and get extra information and see the location highlighted on the map. The actual content is not very deeply developed — there’s enough here that you can travel from port to port, do some trading, and have your hull damaged repeatedly by space debris, but it doesn’t dramatically develop very much more than that.

The structurally interesting bit about Starfreighter as a storylet system is that it looks not just for specific qualities (like Fallen London‘s “if your Connected: the Duchess is greater than 10”) but for resources that fit particular qualities, and subsequently binds those identified resources to the storylet for purposes of producing the narration. So for instance you might have a storylet “sell [cargo] on [planet]”, which would become available if you had any cargo (say, a crate of exotic matter) and were on any planet (say, Uinox), so that the storylet text would then be realized as “sell crate of exotic matter on Uinox.”

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Mailbag: AI Research on Dialogue and Story Generation

I’m curious: do you follow much research that happens in stories and dialog these days? In the world of machine learning research, there’s much less in dialog and stories than other areas (e.g. image generation/recognition or translation), but once in a while, you come across some interesting work, e.g. Hierarchical Neural Story Generation (by some folks in Facebook AI).

For some years now I’ve followed work coming out of the UCSC Expressive Intelligence Studio; work done at Georgia Tech around crowdsourced narrative generation; game industry applications introduced or covered at the GDC AI Summit (though it is rarer to see extensive story-generation work here). I’ve also served on the program committees for ICCC and ICIDS and a few FDG workshops; and am an associate editor on IEEE Transactions on Games focused on interactive storytelling applications. Here (1, 2, 3) is my multi-part post covering the book Interactive Digital Narrative in detail.

That’s not to say I see (or could see) everything that’s happening. I tend to focus on things that look most ready to be used in games, entertainment, or chatbot applications — especially those that are designed to support a partially human-authored experience. I also divide my available “research” time between academic work and hands on experiments in areas that interest me.

So with that perspective in mind:

  • I’m not attempting a comprehensive literature review here! That would be huge. This coverage cherrypicks items
  • I will go pretty lightly on the technical detail since the typical readership of this blog may not be that interested, but I’ll try to provide summary and example information that explains why a given item is interesting in my opinion, and then link back to the original research for people who want the deeper dive
  • I’ll actually start by summarizing a bit the paper the questioner linked
  • Even with cherrypicking, there is a lot to say here and I am breaking it out over multiple posts

That Initial Paper

For other readers: the linked article in this question is about using a large dataset pulled from Reddit’s WritingPrompts board and a machine learning model that draws on multiple techniques (convolutional seq2seq, gated self-attention). After training, the system is able to take short prompts and create a paragraph or so of story that relates to the prompt. Several of the sample output sections are quite cool:

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But they are generating surface text rather than plot, and the evidence suggests that they would not be able to produce a coherent long-term plot. Just within this dialogue section, we’re talking about a tablet-virus-monster object, and we’ve got a couple of random scientist characters.

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