Discovering Social and Aesthetic Categories of Avatars: A Bottom-Up Artificial Intelligence Approach Using Image Clustering

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Lim Chong-U Liapis Antonios Harrell Fox D.
2016 DiGRA/FDG '16 - Proceedings of the First International Joint Conference of DiGRA and FDG

Videogame avatars are more than visual artifacts—they express cultural norms and expectations from both the real world and the fictional world. In this paper, we describe how artificial intelligence clustering can automatically discover distinct characteristics of players’ avatars without prior knowledge of a system’s underlying data structures. Using only avatar images collected from a study with 191 players, we applied two clustering techniques— namely non-negative matrix factorization and archetypal analysis—that automatically revealed and detected (1) an avatar’s gender, (2) regions that appeared to isolate shapes of items and accessories, and (3) aesthetic preferences for particular colors (e.g., bright or muted) and shapes for different body parts. These clusters correlated with players’ preferences for character abilities, e.g., male avatars in dark clothes correlated with having high physical but low magic-casting attributes. These findings show that a bottom-up analysis of images can reveal explicit categories like gender, but also implicit categories like preferences of players. We believe that such computational approaches can enable developers to (1) better understand players’ desires and needs, (2) quantitatively view how systems may be limited in supporting players, and (3) find actionable solutions for these limitations.

 

Playing with Data: Procedural Generation of Adventures from Open Data

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Barros Gabriella A.B. Liapis Antonios Togelius Julian
2016 DiGRA/FDG '16 - Proceedings of the First International Joint Conference of DiGRA and FDG

This paper investigates how to generate simple adventure games using open data. We present a system that creates a plot for the player to follow based on associations between Wikipedia articles which link two given topics (in this case people) together. The Wikipedia articles are transformed into game objects (locations, NPCs and items) via constructive algorithms that also rely on geographical information from OpenStreetMaps and visual content from Wikimedia Commons. The different game objects generated in this fashion are linked together via clues which point to one another, while additional false clues and dead ends are added to increase the exploration value of the final adventure game. This information is presented to the user via a set of game screens and images. Inspired by the “Where in the World is Carmen Sandiego?” adventure game, the end result is a generator of chains of followable clues.