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20162022
most citedLearning-based Preference Prediction for Constrained Multi-Criteria Path-Planning

6 citations · 14 across the 11 of their papers we have counts for

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14 papers · 1 filter

cs.AI20221 cited

Nested Search versus Limited Discrepancy Search

Tristan Cazenave

Limited Discrepancy Search (LDS) is a popular algorithm to search a state space with a heuristic to order the possible actions. Nested Search (NS) is another algorithm to search a…

cs.AI2022

Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks

Kevin Osanlou, Jeremy Frank, Andrei Bursuc +4

Planning under uncertainty is an area of interest in artificial intelligence. We present a novel approach based on tree search and graph machine learning for the scheduling problem…

cs.AI20216 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.AI20211 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…