Volume 41 (2022)

Permanent URI for this community

https://diglib.eg.org/handle/10.2312/2633151

Browse

Browsing Volume 41 (2022) by Subject "agent systems"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Cognitive Model of Agent Exploration with Vision and Signage Understanding
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Johnson, Colin; Haworth, Brandon; Dominik L. Michels; Soeren Pirk
    Signage systems play an essential role in ensuring safe, stress-free, and efficient navigation for the occupants of indoor spaces. Crowd simulations with sufficiently realistic virtual humans provide a convenient and cost-effective approach to evaluating and optimizing signage systems. In this work, we develop an agent model which makes use of image processing on parametric saliency maps to visually identify signage and distractions in the agent's field of view. Information from identified signs is incorporated into a grid-based representation of wayfinding familiarity, which is used to guide informed exploration of the agent's environment using a modified A* algorithm. In areas with low wayfinding familiarity, the agent follows a random exploration behaviour based on sampling a grid of previously observed locations for heuristic values based on space syntax isovist measures. The resulting agent design is evaluated in a variety of test environments and found to be able to reliably navigate towards a goal location using a combination of signage and random exploration.
  • Thumbnail Image
    Item
    Dynamic Combination of Crowd Steering Policies Based on Context
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Cabrero-Daniel, Beatriz; Marques, Ricardo; Hoyet, Ludovic; Pettré, Julien; Blat, Josep; Chaine, Raphaëlle; Kim, Min H.
    Simulating crowds requires controlling a very large number of trajectories of characters and is usually performed using crowd steering algorithms. The question of choosing the right algorithm with the right parameter values is of crucial importance given the large impact on the quality of results. In this paper, we study the performance of a number of steering policies (i.e., simulation algorithm and its parameters) in a variety of contexts, resorting to an existing quality function able to automatically evaluate simulation results. This analysis allows us to map contexts to the performance of steering policies. Based on this mapping, we demonstrate that distributing the best performing policies among characters improves the resulting simulations. Furthermore, we also propose a solution to dynamically adjust the policies, for each agent independently and while the simulation is running, based on the local context each agent is currently in. We demonstrate significant improvements of simulation results compared to previous work that would optimize parameters once for the whole simulation, or pick an optimized, but unique and static, policy for a given global simulation context.