most citedA Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference

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cs.AI2026

Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty

Giovanni Briglia, Marco Lippi, Stefano Mariani +1

Bayesian Networks (BNs) are established tools for letting agents reason under uncertainty in sequential decision-making. They split a joint distribution into local conditional mech…

cs.AI2025

Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics

Giovanni Briglia, Francesco Fabiano, Stefano Mariani

Multi-agent Epistemic Planning (MEP) is an autonomous planning framework for reasoning about both the physical world and the beliefs of agents, with applications in domains where i…