James Owen Ryan

Open Design Challenges for Interactive Emergent Narrative

We introduce a research framework for the design of interactive experiences in the domain of emergent narrative, an application area of computational narrative in which stories emerge bottom-up from the behavior of autonomous characters in a simulated storyworld. Prior work in this area has largely concerned the development and tuning of the simulations themselves from which interesting stories may reliably emerge, but this approach will not necessarily improve system performance at its most crucial level—the actual interactive experience. Looking to completed experiences, namely simulation games like Dwarf Fortress and The Sims, we identify a series of shortcomings that yield four design challenges at the level of interaction: modular content, compositional representational strategies, story recognition, and story support. In this paper, we motivate and discuss each of these design challenges and, for each, summarize prior work and propose new approaches that future work might take.

Expressionist: An Authoring Tool for In-Game Text Generation

We present Expressionist, an authoring tool for in-game text generation that combines the raw generative power of context-free grammars (CFGs) with the expressive power of free-text markup. Specifically, authors use the tool to define CFGs whose nonterminal symbols may be annotated using arbitrary author-defined tagsets. Any content generated by the CFG comes packaged with explicit metadata in the form of the markup attributed to all the symbols that were expanded to produce the content. Expressionist has already been utilized in two released games and it is currently being used in two ongoing projects. In this paper, we describe the tool and discuss these usage examples in a series of case studies. Expressionist is planned for release in late 2016.

Bad News: An Experiment in Computationally Assisted Performance

Dreams of the prospect of computational narrative suggest a future of deeply interactive and personalized fictional experiences that engage our empathy. But the gulf between our current moment and that future is vast. How do we begin to bridge that divide now, both for learning more specifics of these potentials and to create experiences today that can have some of their impact on audiences? We present Bad News, a combination of theatrical performance practices, computational support, and Wizard-of-Oz interaction techniques. These allow for rich, real-time interaction with a procedurally generated world. We believe our approach could enable other research groups to explore similar territory—and the resulting experience is engaging and affecting in ways that help strengthen the case for our envisioned futures and also makes the case for trying to field such experiences today (e.g., in experimental theater or location-based entertainment contexts). Bad News is a realized game enjoyed by players with varying degrees of performance experience, and won the Innovative Game Design track of the 2016 ACM Conference on Human Factors in Computing Systems (CHI) Student Game Competition.

Combinatorial Dialogue Authoring

We present an annotation scheme and combinatorial authoring procedure by which a small base of annotated human-authored dialogue exchanges can be exploited to automatically generate many new exchanges. The combinatorial procedure builds recombinant exchanges by reasoning about individual lines of dialogue in terms of their mark-up, which is attributed during annotation and captures what a line expresses about the story world and what it specifies about lines that may precede or succeed it in new contexts. From a human evaluation task, we find that while our computer-authored recombinant dialogue exchanges are not rated as highly as human-authored ones, they still rate quite well and show more than double the strength of the latter in expressing game state. We envision immediate practical use of our method in a collaborative authoring scheme in which, given a small database of annotated dialogue, the computer instantly generates many full exchanges that the human author then polishes, if necessary. We believe that combinatorial dialogue authoring represents an immediate and huge reduction in authorial burden relative to current authoring practice.

Translating Player Dialogue into Meaning Representations Using LSTMs

In this paper, we present a novel approach to natural language understanding that utilizes context-free grammars (CFGs) in conjunction with sequence-to-sequence (seq2seq) deep learning. Specifically, we take a CFG authored to generate dialogue for our target application, a videogame, and train a long short-term memory (LSTM) recurrent neural network (RNN) to translate the surface utterances that it produces to traces of the grammatical expansions that yielded them. Critically, we already annotated the symbols in this grammar for the semantic and pragmatic considerations that our game’s dialogue manager operates over, allowing us to use the grammatical trace associated with any surface utterance to infer such information. From preliminary offline evaluation, we show that our RNN translates utterances to grammatical traces (and thereby meaning representations) with great accuracy.

1st Workshop on the History of Expressive Systems

The first meeting of a new workshop series on the History of Expressive Systems (HEX) is being held at ICIDS 2017. By ‘expressive systems’, we broadly mean computer systems (or predigital procedural methods) that were developed with expressive or creative aims. HEX is meant to illuminate and celebrate the history of systems in this area, especially the untold histories of projects that are today forgotten or relatively unknown.

Recognizing Coherent Narrative Blog Content

Interactive storytelling applications have at their disposal massive numbers of human-authored stories, in the form of narrative weblog posts, from which story content could be harvested and repurposed. Such repurposing is currently inhibited, however, in that many blog narratives are not sufficiently coherent for use in these applications. In a narrative that is not coherent, the order of the events in the narrative is not clear given the text of the story. We present the results of a study exploring automatic methods for estimating the coherence of narrative blog posts. In the end, our simplest model—one that only considers the degree to which story text is capitalized and punctuated—vastly outperformed a baseline model and, curiously, a series of more sophisticated models. Future work may use this simple model as a baseline, or may use it along with the classifier that it extends to automatically extract large numbers of narrative blog posts from the web for purposes such as interactive storytelling.