6 citations · 30 across the 14 of their papers we have counts for
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cs.AI2020★ 4 cited
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach
Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The…
cs.RO2020★ 6 cited
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Yiming Wang, Francesco Giuliari, Riccardo Berra +5
In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method use…