Inferring User Intent using Bayesian Theory of Mind in Shared Avatar-Agent Virtual Environments.

We present a real-time algorithm to infer the intention of a user's avatar in a virtual environment shared with multiple human-like agents. Our algorithm applies the Bayesian Theory of Mind approach to make inferences about the avatar's hidden intentions based on the observed proxemics and gaze-based cues. Our approach accounts for the potential irrationality in human behavior, as well as the dynamic nature of an individual's intentions. The inferred intent is used to guide the response of the virtual agent and generate locomotion and gaze-based behaviors. Our overall approach allows the user to actively interact with tens of virtual agents from a first-person perspective in an immersive setting. We systematically evaluate our inference algorithm in controlled multi-agent simulation environments and highlight its ability to reliably and efficiently infer the hidden intent of a user's avatar even under noisy conditions. We quantitatively demonstrate the performance benefits of our approach in terms of reducing false inferences, as compared to a prior method. The results of our user evaluation show that 68.18% of participants reported feeling more comfortable in sharing the virtual environment with agents simulated with our algorithm as compared to a prior inference method, likely as a direct result of significantly fewer false inferences and more plausible responses from the virtual agents.

Generating pedestrian trajectories consistent with the fundamental diagram based on physiological and psychological factors.

Pedestrian crowds often have been modeled as many-particle system including microscopic multi-agent simulators. One of the key challenges is to unearth governing principles that can model pedestrian movement, and use them to reproduce paths and behaviors that are frequently observed in human crowds. To that effect, we present a novel crowd simulation algorithm that generates pedestrian trajectories that exhibit the speed-density relationships expressed by the Fundamental Diagram. Our approach is based on biomechanical principles and psychological factors. The overall formulation results in better utilization of free space by the pedestrians and can be easily combined with well-known multi-agent simulation techniques with little computational overhead. We are able to generate human-like dense crowd behaviors in large indoor and outdoor environments and validate the results with captured real-world crowd trajectories.