Everything in Context

Hello from 2021! It's now been almost three years(!) since I last posted here, as I've gotten caught up in adjusting to faculty life. Without making any promises for the future, I have some ideas for revitalizing this blog, or otherwise regularly updating the public on my & my lab's professional activities. I'd like to start with a report on what my students accomplished in 2020. Despite 2020 being a challenging year for everyone, my POEM lab crew -- a combination of PhD, masters, and undergraduate research students -- accomplished some truly amazing things. In the interest of celebrating accomplishments, I asked my students to each share something they were proud of from the preceding year. Their responses are below, supplemented with a bit of my own bragging. Claire Aguiar , a sophomore undergraduate student, joined a cross-disciplinary team with Political Science faculty and students to help us study the potential for board games to operationalize, explore, and

I have now been advising (computer science) PhD students for about a year and a half. This is just long enough, combined with my own recent experience of being  a PhD student and watching those around me, to have encountered some repeated patterns of stumbling blocks and growth and to start thinking about whether there are ways to support students in a more scalable way than through one-on-one advising. One-on-one, face-to-face advising is widely considered (and in my opinion) of the most valuable parts of doing a PhD; in few other professions does one have an opportunity to receive this kind of personally-tailored mentorship through topics of intellectual curiosity. However, it has its pitfalls. One of the most common stumbling blocks for students is that the advice, guidance, structure, and content of education provided to students by one advisor differs dramatically from that of another advisor, even in the same field. Meanwhile, the PhD program run by the student's department

Content note: I replicate some ableist language in this post for the sake of calling it out as ableist. In games research, some people take pains to distinguish artificial intelligence  from computational intelligence  ( Wikipedia summary ), with the primary issue being that AI cares more about replicating human behavior, while CI is "human-behavior-inspired" approaches to solving concrete problems. I don't strongly identify with one of these sub-areas more than the other; the extent to which I hold an opinion is mainly that I find the distinction a bit silly, given that the practical effects seem mainly to be that there are two conferences (CIG and AIIDE) that attract the same people, and a journal (TCIAIG - Transactions on Computational Intelligence and Artificial Intelligence in Games) that seems to resolve the problem by replacing instances of "AI" with "CI/AI." I have a vague, un-citeable memory of hearing another argument from people who dis

This past week I attended (and co-chaired, organized a workshop for, and presented at) the International Conference on Interactive Digital Storytelling ( ICIDS ). In celebration of a successful ICIDS, I'll share a Paper and Game of the Week each of which I discovered during it. Paper of the Week: "Using BDI to Model Players Behaviour in an Interactive Fiction Game" By Jessica Rivera-Villicana, Fabio Zambetta, James Harland, and Marsha Berry; available for download on ResearchGate . Disclaimer : I attended the talk about this paper, but have only skimmed the text of the paper itself. Player modeling  is a sub-area of game AI concerned with representing and tracking players' mental states and experiences while playing a game. This is the first paper I've seen addressing the problem in an interactive narrative context. BDI  stands for Belief, Desire, and Intention, a philosophical framework for agent modeling (from 1987) that supposes all actions are driven

Paper of the Week: Imaginative Recall with Story Intention Graphs By Sarah Harmon and Arnav Jhala . Bias disclaimer: both Sarah and Arnav are folks I've worked with and consider colleagues. Imaginative recall  is the process of generalizing and extrapolating previously-seen narrative examples to create new ones. Harmon and Jhala present an automated system for carrying out this process based on a narrative representation scheme called story intention graphs (SIGs). This paper was a bit hard for me to tease apart initially because there are really two things going on: The system of imaginative recall originally embodied by the Minstrel system, later adapted to the modern rewrite Skald, which uses case-based reasoning The translation between Skald story representation and SIGs (1) is largely prior work, but lays the groundwork and motivation for their project. Ultimately they are interested in the problem of carrying out processes on narratives such as adaptation, t

I'm going to try to loosen the ol' blogging joints a bit by experimenting with a weekly feature: a Paper and Game of the Week, posted every Friday morning. My goal will be to keep a record of recent research inspirations in the hopes of exposing interesting gems to others, and providing better context for my own work. I will preemptively establish the expectation that the paper and game of the week may not *literally* be a paper and a game; the objective is more like "something CS-academia-centered" and "something creativity/arts-movement centered," which for me in recent weeks has mostly meant papers and games, but at other times has included talks, interactive essays, plays, art exhibits, and weird internet art. So without further ado: Paper of the Week Commonsense Interpretation of Triangle Behavior  by Andrew S. Gordon (not Andrew D. Gordon , although his papers might easily feature on this blog too). This paper is a formalization of the reas