# Probabilistic Plan Synthesis for Coupled Multi-Agent Systems

arXiv: https://arxiv.org/abs/1704.01432 · HTML: https://mlsanity.com/paper/1704.01432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [Formal Modelling for Multi-Robot Systems Under Uncertainty](https://mlsanity.com/paper/2305.17018/) (2023)

## Abstract (arXiv)

This paper presents a fully automated procedure for controller synthesis for multi-agent systems under the presence of uncertainties. We model the motion of each of the N agents in the environment as a Markov Decision Process (MDP) and we assign to each agent one individual high-level formula given in Probabilistic Computational Tree Logic (PCTL). Each agent may need to collaborate with other agents in order to achieve a task. The collaboration is imposed by sharing actions between the agents. We aim to design local control policies such that each agent satisfies its individual PCTL formula. The proposed algorithm builds on clustering the agents, MDP products construction and controller policies design. We show that our approach has better computational complexity than the centralized case, which traditionally suffers from very high computational demands.