Publication
Multi-Agent Causal Reinforcement Learning
André Meyer-Vitali
In: Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - MBSE-AI Integration. International Conference on Model-Driven Engineering and Software Development (MODELSWARD-2025), MBSE-AI Integration, February 26-28, Porto, Portugal, Pages 435-442, ISBN 978-989-758-729-0, SciTePress, 2/2025.
Abstract
It has become clear that mere correlations extracted from data through statistical processes are insufficient to give insight into the causal relationships inherent in them. Causal models support the necessary understanding of these relationships to make transparent and robust decisions. In a distributed setting, the causal models that are shared between agents improve their coordination and collaboration. They learn individually and from each other to optimise a system’s behaviour. We propose a combination of causal models and multi-agent reinforcement learning to create reliable and trustworthy AI systems. This combination strengthens the modelling and reasoning of agents that communicate and collaborate using shared causal insights. A comprehensive method for applying and integrating these aspects is being developed.