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Collaborating Authors

 Brdiczka, Oliver


What's Hot in Intelligent User Interfaces

AAAI Conferences

The ACM Conference on Intelligent User Interfaces (IUI) is the annual meeting of the intelligent user interface community and serves as a premier international forum for reporting outstanding research and development on intelligent user interfaces. ACM IUI is where the Human-Computer Interaction (HCI) community meets the Artificial Intelligence (AI) community. Here we summarize the latest trends in IUI based on our experience organizing the 20th ACM IUI Conference in Atlanta in 2015. At ACM IUI, we address the complex interactions between Figure 1: Take a Selfie with Hairware machine intelligence and human intelligence by leveraging solutions from machine learning, knowledge representation and new interaction technologies. Although submissions focusing paradigms have emerged. For example, at IUI 2015, conductive on only Artificial Intelligence (AI) or Human Computer hair extensions were used to send messages, record Interaction (HCI) will be considered, we give strong conversations and control cameras (Vega, Cunha, and Fuks preferences to submissions that discuss research from both 2015) (Figure 1).


Modeling Destructive Group Dynamics in On-Line Gaming Communities

AAAI Conferences

Social groups often exhibit a high degree of dynamism. Some groups thrive, while many others die over time. Modeling destructive dynamics and understanding whether/why/when a person will depart from a group can be important in a number of social domains. In this paper, we take the World of Warcraft game as an exemplar platform for studying destructive group dynamics. We build models to predict if and when an individual is going to quit his/her guild, and whether this quitting event will inflict substantial damage on the guild. Our predictors start from in-game census data and extract features from multiple perspectives such as individual-level, guild-level, game activity, and social interaction features. Our study shows that destructive group dynamics can often be predicted with modest to high accuracy, and feature diversity is critical to prediction performance.