We explored human-virtual agent collaboration during a layout design task in a virtual reality environment. Specifically, we developed a human-in-the-loop optimization-based method that drives the decision-making of the virtual agent. Our algorithm accounts for spatial constraints in furniture placement by evaluating boundary proximity, collision costs, and relationships between furniture items in real-time. It also considers the current configuration of the living room, as modified by the user during the co-design process, to guide the virtual agent's furniture placement decisions in the virtual living room. We compared our method (i.e., optimization) against two other co-design strategies (i.e., template and random) following a within-group ($N=24)$study design. We found the proposed optimization co-design strategy significantly enhanced perceived collaboration compared to the other two co-design strategies. Moreover, our participants attributed higher private and public awareness to the virtual agent in the optimization condition. In addition, the analysis of the logged data showed that participants placed more furniture items and made fewer corrections when codesigning the living room with a virtual agent whose decisions were based on the optimization method. Our results demonstrate that a virtual agent's behavior, which dynamically responds to user actions while maintaining spatial coherence, creates more effective collaborative experiences in an immersive co-design task.

Researchers have provided insights into using virtual reality (VR) for visualization and interaction with 3D models and simulations. The interaction allows users to manipulate the 3D elements and visualize changes based on their inputs from movement with controllers or spatial actions. However, some users may find this interaction overwhelming, especially when immersed in a virtual environment. Additionally, the choice of dimensionality for visualizations influences user interaction, with potential implications for immersive experiences. Thus, we conducted a 2 (Immersion: Desktop vs. HMDVR) $\times 2$ (Dimensionality: 2 D vs. 3 D) within-group study $(N=32)$ to explore the impact of the utilized immersive degree and the dimensionality representation of the content on participants’ experience in terms of engagement, task load, usability, skill, and emotions when interacting with a science simulation. We designed and developed an application to simulate charged particles and electric field lines. We asked participants to complete a task of changing particles by matching them to a given simulation output. Our results indicated higher workload rates for HMDVR conditions, particularly with 3D representation, compared to Desktop. However, HMDVR conditions also showed greater engagement, emotional response, and presence. Based on our findings, we argue that participants prefer HMDVR over Desktop environments regardless of dimensionality.

Synthesizing game levels that evoke players’ curiosity, driving them to explore different level parts, is time-consuming and tedious. Typically, game level designers manually perform this synthesis using trial and error. In this paper, we propose a method with which to replace this manual, time-consuming process. We benefited from recent work that had proposed game level design patterns to evoke curiosity, and we propose an approach to automatically synthesizing game levels in order to encourage players to pursue designer-specified exploration goals. We started by creating a dataset of level assets, based on the four design patterns that evoke curiosity-driven exploration in games (reaching extreme points, resolving visual obstructions, out-of-place objects, and understanding spatial connections). We annotated the assets in our dataset with spatial exploration measurements (the time players took to explore an asset over their total time spent in the game level). We then formulated game level design as an optimization problem, encoding both spatial exploration (mean spatial exploration, spatial exploration variance, and spatial exploration distribution) and game level design (occupied area, adjacent penalty, and height distribution) decisions. Then, we solved this problem by implementing a reversible-jump Markov chain Monte Carlo method. We demonstrate our method’s ability to synthesize game level variations with different spatial exploration and level design decisions. Finally, a user study showed that our approach can automatically synthesize game levels, encouraging a certain amount of spatial exploration by players.