This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles.
In this paper, we synthesize existing work from the Intelligent User Interface and Learning Science research communities, where they started to investigate the potential of making such data and models available to users.
In this paper, we focus on the adversarial player strategy aspect in the game iNNk, in which players try to communicate secret code words through drawings with the goal of not being deciphered by a NN.
Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment.
This paper explores multi-armed bandit (MAB) strategies in very short horizon scenarios, i. e., when the bandit strategy is only allowed very few interactions with the environment.
This paper focuses on building personalized player models solely from player behavior in the context of adaptive games.
In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change.
The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research.
no code implementations • 17 Jul 2020 • Jennifer Villareale, Ana Acosta-Ruiz, Samuel Arcaro, Thomas Fox, Evan Freed, Robert Gray, Mathias Löwe, Panote Nuchprayoon, Aleksanteri Sladek, Rush Weigelt, Yifu Li, Sebastian Risi, Jichen Zhu
This paper presents iNNK, a multiplayer drawing game where human players team up against an NN.
This paper focuses on "tracing player knowledge" in educational games.
Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals.