Predictive & Trust-based Multi-Agent Coordination

Fuente: arXiv
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Auteurs principaux: Renganathan, Venkatraman, Mondal, Sabyasachi, Tsourdos, Antonios
Format: Preprint
Publié: 2025
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author Renganathan, Venkatraman
Mondal, Sabyasachi
Tsourdos, Antonios
author_facet Renganathan, Venkatraman
Mondal, Sabyasachi
Tsourdos, Antonios
contents This paper presents a trust-based predictive multi-agent consensus protocol that analyses neighbours' anticipation data and makes coordination decisions. Agents in the network share their future predicted data over a finite look-ahead horizon with their neighbours and update their predictions in a rolling-horizon fashion. The prediction data is then used by agents to learn both the trust and the commitment traits exhibited by their neighbours over time. The proposed protocol is named as the Anticipatory Distributed Coordination (ADC) protocol. Lyapunov theory-based agreement convergence between agents is provided, followed by demonstrations using numerical simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2507_09997
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Predictive & Trust-based Multi-Agent Coordination
Renganathan, Venkatraman
Mondal, Sabyasachi
Tsourdos, Antonios
Systems and Control
Optimization and Control
This paper presents a trust-based predictive multi-agent consensus protocol that analyses neighbours' anticipation data and makes coordination decisions. Agents in the network share their future predicted data over a finite look-ahead horizon with their neighbours and update their predictions in a rolling-horizon fashion. The prediction data is then used by agents to learn both the trust and the commitment traits exhibited by their neighbours over time. The proposed protocol is named as the Anticipatory Distributed Coordination (ADC) protocol. Lyapunov theory-based agreement convergence between agents is provided, followed by demonstrations using numerical simulations.
title Predictive & Trust-based Multi-Agent Coordination
topic Systems and Control
Optimization and Control
url https://arxiv.org/abs/2507.09997