A Card Game for Collecting Human-Perceived Similarity Data of Artwork Images

Abstract

In this paper, we propose a card game system named “Lottery” for collecting similarity data of artwork images. It is built based on the concept of game design called “Audience Participation Game With a Purpose (APGWAP),” which is to outsource computational steps in a given task to humans via a game that allows audiences to participate in a meaningful way. The game system is streamed on Twitch. In this game system, two Artificial Intelligence (AI) players match two cards that are most similar from a set of cards in their turn and discard them. The audiences can choose their roles between Assistant and Jury. Assistant helps the AI player of a given turn choose such a pair of cards, while Jury gives a score telling how similar a chosen pair is. In the experiments, different methods for providing rewards to audiences are investigated: equal reward, random reward, and performance-based reward. Experimental results show that the performance-based reward significantly promotes personal gratification and provides a better gaming experience on several other factors. In addition, it was found in our experiment that different groups of humans provided similar similarity scores for all the pairs of images, indicating that collecting promises reliable human data that are consistent through several trials is possible using our game system.