This paper presents a virtual reality (VR) study that examines how multiple road users jointly interact with an autonomous vehicle (AV) in complex traffic scenarios. Moving beyond dyadic studies (e.g., AV-pedestrian or AV-passenger), our multi-user setup simulates ambiguous all-way stop intersections involving a pedestrian, a human driver in a conventional vehicle, and a passenger in an AV, all interacting simultaneously with the AV. We investigated how users perceive and respond to two distinct types of AV behaviors: an efficient AV that proceeds as soon as it is safe to do so, and a prosocial AV that yields to others before entering the intersection. Sixteen groups of three participants (N=48) took part in the study, with each group interacting with a single AV type across four ambiguous traffic scenarios. Our findings show that even simple AV behavior logics can meaningfully shape crossing negotiation dynamics and highlight how trust and perception can vary across different user roles. We conclude by discussing how our methods and insights can inform the research and design of AV interactions in complex multi-agent traffic environments.

Personal intelligent agents (IAs) are increasingly embedded in everyday life, a trend accelerated by generative AI technologies. Despite their growing presence, these agents often remain fragmented across different life domains and environments. This workshop explores how to design integrated IA ecosystems emphasizing continuity, coordination, and human-centered values. Participants with varied perspectives will collaboratively develop frameworks, scenarios, and guidelines for cohesive personal agent systems that enrich user experiences holistically. By examining factors that shape users' preferences for information integration or separation, we aim to inform the design of coherent, user-aligned multi-agent systems.

We present an interactive virtual agent that employs politeness strategies to suggest compromises during a joint decision-making task with a human partner.In our approach, politeness is expressed not only through language, but also through the agent's willingness to compromise by proposing options that better align with the human partner's preferences.We conducted a within-participant experiment (𝑛 = 97) in which users interacted with two types of agents: a performance-optimizing agent and a socially considerate agent.The former prioritized decision quality, while the latter occasionally made socially motivated quality concessions to the user as a form of face-work to defer to the human partner.Our results show that participants reported higher satisfaction with the final decision and consistent intention to collaborate with the socially considerate agent on both tasks.However, the social agent was less effective than the performance-optimizing agent at improving the users' initial decisions.These findings highlight trade-offs in human-agent interaction between optimizing task performance and managing the social dynamics of collaboration.We conclude by discussing the ethical and cultural considerations that may be involved in navigating this trade-off.

We describe a physical interactive system for human-robot collaborative design (HRCD) consisting of a tangible user interface (TUI) and a robotic arm that simultaneously manipulates the TUI with the human designer. In an observational study of 12 participants exploring a complex design problem together with the robot, we find that human designers have to negotiate both the physical and the creative space with the machine. They also often ascribe social meaning to the robot's pragmatic behaviors. Based on these findings, we propose four considerations for future HRCD systems: managing the shared workspace, communicating preferences about design goals, respecting different design styles, and taking into account the social meaning of design acts.