STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents
CoRR(2024)
摘要
Learning accurate, data-driven predictive models for multiple interacting
agents following unknown dynamics is crucial in many real-world physical and
social systems. In many scenarios, dynamics prediction must be performed under
incomplete observations, i.e., only a subset of agents are known and observable
from a larger topological system while the behaviors of the unobserved agents
and their interactions with the observed agents are not known. When only
incomplete observations of a dynamical system are available, so that some
states remain hidden, it is generally not possible to learn a closed-form model
in these variables using either analytic or data-driven techniques. In this
work, we propose STEMFold, a spatiotemporal attention-based generative model,
to learn a stochastic manifold to predict the underlying unmeasured dynamics of
the multi-agent system from observations of only visible agents. Our analytical
results motivate STEMFold design using a spatiotemporal graph with time anchors
to effectively map the observations of visible agents to a stochastic manifold
with no prior information about interaction graph topology. We empirically
evaluated our method on two simulations and two real-world datasets, where it
outperformed existing networks in predicting complex multiagent interactions,
even with many unobserved agents.
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