RESUMO
Interactive data visualization plays a crucial role in the interpretability of large datasets. Virtual reality offers unique advantages in exploring data, beyond traditional 2-D views. This article presents a set of interaction artifacts designed for analyzing and interpreting complex datasets through immersive 3-D graph visualization and interaction. Our system makes complex datasets easier to work with by offering a wide range of visual customization tools and intuitive methods for selection, manipulation, and filtering. It also provides a cross-platform, collaborative environment that can be accessed by remote users through traditional computers, drawing tablets, and touchscreen devices.
RESUMO
Many technical and psychological challenges make it difficult to design machines that effectively cooperate with people. To better understand these challenges, we conducted a series of studies investigating human-human, robot-robot, and human-robot cooperation in a strategically rich resource-sharing scenario, which required players to balance efficiency, fairness, and risk. In these studies, both human-human and robot-robot dyads typically learned efficient and risky cooperative solutions when they could communicate. In the absence of communication, robot dyads still often learned the same efficient solution, but human dyads achieved a less efficient (less risky) form of cooperation. This difference in how people and machines treat risk appeared to discourage human-robot cooperation, as human-robot dyads frequently failed to cooperate without communication. These results indicate that machine behavior should better align with human behavior, promoting efficiency while simultaneously considering human tendencies toward risk and fairness.