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Found 15 papers, 1 papers with code
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey
Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Improving Fairness in Adaptive Social Exergames via Shapley Bandits
Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.
Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space
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
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
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.
The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems
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
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
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.
Player Modeling via Multi-Armed Bandits
This paper focuses on building personalized player models solely from player behavior in the context of adaptive games.
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity
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.
Player-AI Interaction: What Neural Network Games Reveal About AI as Play
The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research.
iNNk: A Multi-Player Game to Deceive a Neural Network
This paper presents iNNK, a multiplayer drawing game where human players team up against an NN.
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples
AI algorithms are not immune to biases.
Tracing Player Knowledge in a Parallel Programming Educational Game
This paper focuses on "tracing player knowledge" in educational games.
Experience Management in Multi-player Games
Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals.
