Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning.

Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.

Gamification of Crowd-Driven Environment Design.

This article explores whether crowd-sourced human creativity within a gamified collaborative design framework can address the complexity of predictive environment design. This framework is predicated on gamifying crowd objectives and presenting environment design problems as puzzles. A usability study reveals that the framework is considered usable for the task. Participants were asked to configure an environment puzzle to reduce an important crowd metric, the total egress time. The design task was constructed to be straightforward and uses a simplified environment as a probe for understanding the utility of gamification and the performance of collaboration. Single-player and multiplayer designs outperformed both optimization and expert-sourced designs of the same environment and multiplayer designs further outperformed the single-player designs. Single-player and multiplayer iterations followed linear and exponential decrease trends in total egress time, respectively. Our experiments provide strong evidence toward an interesting novel approach of crowdsourcing collaborative environment design.

Interactive Architectural Design with Diverse Solution Exploration.

In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.

Evaluating and Optimizing Evacuation Plans for Crowd Egress.

Evacuation planning is an important and difficult task in building design. The proposed framework can identify optimal evacuation plans using decision points, which control the ratio of agents that select a particular route at a specific spatial location. The authors optimize these ratios to achieve the best evacuation based on a quantitatively validated metric for evacuation performance. This metric captures many of the important aspects of an evacuation: total evacuation time, average evacuation time, agent speed, and local agent density. The proposed approach was validated using a night club model that incorporates real data from an actual evacuation.