STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents
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arXiv
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| Format: | Preprint |
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2024
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| author | Kumawat, Hemant Chakraborty, Biswadeep Mukhopadhyay, Saibal |
| author_facet | Kumawat, Hemant Chakraborty, Biswadeep Mukhopadhyay, Saibal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents Kumawat, Hemant Chakraborty, Biswadeep Mukhopadhyay, Saibal Multiagent Systems 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. |
| title | STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2401.14522 |