Search Results for author: Jichen Zhu

Found 13 papers, 1 papers with code

Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space

1 code implementation1 Apr 2022 Imke Grabe, Jichen Zhu, Manex Agirrezabal

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.

Open Player Modeling: Empowering Players through Data Transparency

no code implementations12 Oct 2021 Jichen Zhu, Magy Seif El-Nasr

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.

Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning

no code implementations5 Jul 2021 Mathias Löwe, Jennifer Villareale, Evan Freed, Aleksanteri Sladek, Jichen Zhu, Sebastian Risi

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.

Ensemble Learning Transfer Learning

The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems

no code implementations2 Mar 2021 Santiago Ontañón, Jichen Zhu

Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment.

Human-Computer Interaction

Player-Centered AI for Automatic Game Personalization: Open Problems

no code implementations15 Feb 2021 Jichen Zhu, Santiago Ontañón

Computer games represent an ideal research domain for the next generation of personalized digital applications.

Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits

no code implementations10 Feb 2021 Robert C. Gray, Jichen Zhu, Santiago Ontañón

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.

Multi-Armed Bandits

Player Modeling via Multi-Armed Bandits

no code implementations10 Feb 2021 Robert C. Gray, Jichen Zhu, Dannielle Arigo, Evan Forman, Santiago Ontañón

This paper focuses on building personalized player models solely from player behavior in the context of adaptive games.

Multi-Armed Bandits

Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity

no code implementations25 Jan 2021 Jichen Zhu, Diane H. Dallal, Robert C. Gray, Jennifer Villareale, Santiago Ontañón, Evan M. Forman, Danielle Arigo

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.

Multi-Armed Bandits

Player-AI Interaction: What Neural Network Games Reveal About AI as Play

no code implementations15 Jan 2021 Jichen Zhu, Jennifer Villareale, Nithesh Javvaji, Sebastian Risi, Mathias Löwe, Rush Weigelt, Casper Harteveld

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research.

Experience Management in Multi-player Games

no code implementations4 Jul 2019 Jichen Zhu, Santiago Ontañón

Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals.

Recommendation Systems

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