5 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.AI2024★ 5 cited
Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
Daniele Meli, Alberto Castellini, Alessandro Farinelli
Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distr…
cs.AI2023★ 3 cited
Learning Logic Specifications for Soft Policy Guidance in POMCP
Giulio Mazzi, Daniele Meli, Alberto Castellini +1
Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by com…