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Dynamic combination of crowd steering policies based on context

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dc.contributor.author Cabrero Daniel, Beatriz
dc.contributor.author Marques, Ricardo
dc.contributor.author Hoyet, Ludovic
dc.contributor.author Pettré, Julien
dc.contributor.author Blat, Josep
dc.date.accessioned 2024-02-07T07:09:29Z
dc.date.issued 2022
dc.identifier.citation Cabrero-Daniel B, Marques R, Hoyet L, Pettré J, Blat J. Dynamic combination of crowd steering policies based on context. Comput Graph Forum. 2022;41(2):209-19. DOI: 10.1111/cgf.14469
dc.identifier.issn 0167-7055
dc.identifier.uri http://hdl.handle.net/10230/58977
dc.description.abstract 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Wiley
dc.relation.ispartof Comput Graph Forum. 2022;41(2):209-19
dc.rights This is the peer reviewed version of the following article: Cabrero-Daniel B, Marques R, Hoyet L, Pettré J, Blat J. Dynamic combination of crowd steering policies based on context. Comput Graph Forum. 2022;41(2):209-19, which has been published in final form at http://dx.doi.org/10.1111/cgf.14469. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
dc.title Dynamic combination of crowd steering policies based on context
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1111/cgf.14469
dc.subject.keyword CCS Concepts
dc.subject.keyword Motion path planning
dc.subject.keyword Agent / discrete models
dc.subject.keyword Multi-agent systems
dc.type.version info:eu-repo/semantics/acceptedVersion
dc.embargo.liftdate 2122-05-24

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