Correct-by-Design Control Synthesis of Stochastic Multi-agent Systems: a Robust Tensor-based Solution

Ruohan Wang; Siyuan Liu; Zhiyong Sun; Sofie Haesaert

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arXiv · 2511.06873

Correct-by-Design Control Synthesis of Stochastic Multi-agent Systems: a Robust Tensor-based Solution

Ruohan Wang·Siyuan Liu·Zhiyong Sun·Sofie Haesaert

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Abstract

Discrete-time stochastic systems with continuous spaces are hard to verify and control, even with MDP abstractions due to the curse of dimensionality. We propose an abstraction-based framework with robust dynamic programming mappings that deliver control strategies with provable lower bounds on temporal-logic satisfaction, quantified via approximate stochastic simulation relations. Exploiting decoupled dynamics, we reveal a Canonical Polyadic Decomposition tensor structure in value functions that makes dynamic programming scalable. The proposed method provides correct-by-design probabilistic guarantees for temporal logic specifications. We validate our results on continuous-state linear stochastic systems.

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Ruohan Wang, Siyuan Liu, Zhiyong Sun, Sofie Haesaert. 2025-11-10. Correct-by-Design Control Synthesis of Stochastic Multi-agent Systems: a Robust Tensor-based Solution. https://arxiv.org/abs/2511.06873

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