Although these commercial and research systems are still in testing, it is important to understand how AVs are being marketed to the general public and how they are perceived, so that one day they may be effectively adopted into everyday life. People do not want to see a car they do not trust on the same roads as them, so the questions are: why don’t people trust them, and how can companies and researchers improve the trustworthiness of the vehicles?
Careful considerations in designing and organizing information for restaurant point-of-sale (POS) systems can affect user experience. Unfortunately, usability guidelines are sparse for these systems. Applications from other studies, such as categorical organization and F-shape, are implemented in an experimental interface as a starting point of discussion. A control interface was designed after the default version of NCR Aloha’s POS program: Aloha Table Service. Novice and expert order taking strategies were also observed to compare input differences. This study examined selection time, total time, and selection accuracy across both order and interface types. The results show that time and number of key presses are significantly reduced under the treatment interface, and that teaching expert order taking strategies to novice users may help reduce cognitive load.
With the increasing popularity of AI and machine learning, human-AI teaming has a wide range of applications in transportation, healthcare, the military, manufacturing, and people’s everyday life. Measurement of human-AI team effectiveness is essential for guiding the design of AI and evaluating human-AI teams. To develop suitable measures of human-AI teamwork effectiveness, we created a search and rescue task environment in Minecraft, in which Artificial Social Intelligence (ASI) agents inferred human teams’ mental states, predicted their actions, and intervened to improve their teamwork (Huang et al., 2022). As a comparison, we also collected data from teams with a human advisor and with no advisor. We investigated the effects of human advisor interventions on team performance. In this study, we examined intervention data and compliance in a human-AI teaming experiment to gain insights into the efficacy of advisor interventions. The analysis categorized the types of interventions provided by a human advisor and the corresponding compliance. The finding of this paper is a preliminary step towards a comprehensive study on ASI agents, in which results from the human advisor study can provide valuable comparisons and insights. Future research will focus on analyzing ASI agents’ interventions to determine their effectiveness, identify the best measurements for human-AI teamwork effectiveness, and facilitate the development of ASI agents.
I compared scores of situational awareness to mission performance scores from the Human-Robot Interaction Lab at the ASU campus. This study uses Roblox in a virtual environment to simulate a search and rescue environment. Higher situational awareness was seen to be positively correlated with mission performance scores, but the study is yet to be complete.