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20162025
most citedOptimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search

14 citations · 52 across the 22 of their papers we have counts for

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Showing 2021 · cs.AIShow all

9 papers · 2 filters

cs.AI2021★ 1 cited

Generalized Nested Rollout Policy Adaptation with Dynamic Bias for Vehicle Routing

Julien Sentuc, Tristan Cazenave, Jean-Yves Lucas

In this paper we present an extension of the Nested Rollout Policy Adaptation algorithm (NRPA), namely the Generalized Nested Rollout Policy Adaptation (GNRPA), as well as its use…

cs.AI2021★ 6 cited

Learning-based Preference Prediction for Constrained Multi-Criteria Path-Planning

Kevin Osanlou, Christophe Guettier, Andrei Bursuc +2

Learning-based methods are increasingly popular for search algorithms in single-criterion optimization problems. In contrast, for multiple-criteria optimization there are significa…

cs.AI2021

Time-based Dynamic Controllability of Disjunctive Temporal Networks with Uncertainty: A Tree Search Approach with Graph Neural Network Guidance

Kevin Osanlou, Jeremy Frank, J. Benton +4

Scheduling in the presence of uncertainty is an area of interest in artificial intelligence due to the large number of applications. We study the problem of dynamic controllability…

cs.AI2021

Constrained Shortest Path Search with Graph Convolutional Neural Networks

Kevin Osanlou, Christophe Guettier, Andrei Bursuc +2

Planning for Autonomous Unmanned Ground Vehicles (AUGV) is still a challenge, especially in difficult, off-road, critical situations. Automatic planning can be used to reach missio…

cs.AI2021★ 14 cited

Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search

Kevin Osanlou, Andrei Bursuc, Christophe Guettier +2

Deep learning-based methods are growing prominence for planning purposes. In this paper, we present a hybrid planner that combines a graph machine learning model and an optimal sol…

cs.AI2021★ 1 cited

Batch Monte Carlo Tree Search

Tristan Cazenave

Making inferences with a deep neural network on a batch of states is much faster with a GPU than making inferences on one state after another. We build on this property to propose…