1 citations · 1 across the 2 of their papers we have counts for
3 papers
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…
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…
A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference
Giovanni Briglia, Stefano Mariani, Franco Zambonelli
Causal reasoning is increasingly used in Reinforcement Learning (RL) to improve the learning process in several dimensions: efficacy of learned policies, efficiency of convergence,…