Matthew Guzdial

Zijin Luo, Matthew Guzdial, Mark Riedl

The ability to extract sequences of game events for high-resolution e-sport games has traditionally required access to the game's engine. This serves as a barrier to groups who don't possess this access. It is possible to apply deep learning to derive these logs from gameplay video, but it requires computational power that serves as an additional barrier. These groups would benefit from access to these logs, such as small e-sport tournament organizers who could better visualize gameplay to inform both audience and commentators. In this paper we present a combined solution to reduce the required computational resources and time to apply a convolutional neural network (CNN) to extract events from e-sport gameplay videos. This solution consists of techniques to train a CNN faster and methods to execute predictions more quickly. This expands the types of machines capable of training and running these models, which in turn extends access to extracting game logs with this approach. We evaluate the approaches in the domain of DOTA2, one of the most popular e-sports. Our results demonstrate our approach outperforms standard backpropagation baselines.

Matthew Guzdial, Mark Riedl

Machine learning has been applied to a number of creative, design-oriented tasks. However, it remains unclear how to best empower human users with these machine learning approaches, particularly those users without technical expertise. In this paper we propose a general framework for turn-based interaction between human users and AI agents designed to support human creativity, called {co-creative systems}. The framework can be used to better understand the space of possible designs of co-creative systems and reveal future research directions. We demonstrate how to apply this framework in conjunction with a pair of recent human subject studies, comparing between the four human-AI systems employed in these studies and generating hypotheses towards future studies.

Kristin Siu, Matthew Guzdial, Mark O. Riedl

Human computation games (HCGs) can provide novel solutions to intractable computational problems, help enable scientific breakthroughs, and provide datasets for artificial intelligence. However, our knowledge about how to design and deploy HCGs that appeal to players and solve problems effectively is incomplete. We present an investigatory HCG based on Super Mario Bros. We used this game in a human subjects study to investigate how different social conditions---singleplayer and multiplayer---and scoring mechanics---collaborative and competitive---affect players' subjective experiences, accuracy at the task, and the completion rate. In doing so, we demonstrate a novel design approach for HCGs, and discuss the benefits and tradeoffs of these mechanics in HCG design.