27 citations · 59 across the 6 of their papers we have counts for
7 papers
Learning Efficiency Meets Symmetry Breaking
Yingbin Bai, Sylvie Thiebaux, Felipe Trevizan
Learning-based planners leveraging Graph Neural Networks can learn search guidance applicable to large search spaces, yet their potential to address symmetries remains largely unex…
Learning Domain-Independent Heuristics for Grounded and Lifted Planning
Dillon Z. Chen, Sylvie Thiébaux, Felipe Trevizan
We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular…
Heuristic Search for Multi-Objective Probabilistic Planning
Dillon Chen, Felipe Trevizan, Sylvie Thiébaux
Heuristic search is a powerful approach that has successfully been applied to a broad class of planning problems, including classical planning, multi-objective planning, and probab…
Learning Domain-Independent Planning Heuristics with Hypergraph Networks
William Shen, Felipe Trevizan, Sylvie Thiébaux
We present the first approach capable of learning domain-independent planning heuristics entirely from scratch. The heuristics we learn map the hypergraph representation of the del…
ASNets: Deep Learning for Generalised Planning
Sam Toyer, Felipe Trevizan, Sylvie Thiébaux +1
In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural networ…
Action Schema Networks: Generalised Policies with Deep Learning
Sam Toyer, Felipe Trevizan, Sylvie Thiébaux +1
In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the…