70 citations · 249 across the 13 of their papers we have counts for
10 papers · 1 filter
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…
Reward Potentials for Planning with Learned Neural Network Transition Models
Buser Say, Scott Sanner, Sylvie Thiébaux
Optimal planning with respect to learned neural network (NN) models in continuous action and state spaces using mixed-integer linear programming (MILP) is a challenging task for br…
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…
Numerical Integration and Dynamic Discretization in Heuristic Search Planning over Hybrid Domains
Miquel Ramirez, Enrico Scala, Patrik Haslum +1
In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required th…
Supply Restoration in Power Distribution Systems - A Case Study in Integrating Model-Based Diagnosis and Repair Planning
Sylvie Thiebaux, Marie-Odile Cordier, Olivier Jehl +1
Integrating diagnosis and repair is particularly crucial when gaining sufficient information to discriminate between several candidate diagnoses requires carrying out some repair a…
Anytime State-Based Solution Methods for Decision Processes with non-Markovian Rewards
Sylvie Thiebaux, Froduald Kabanza, John Slanley
A popular approach to solving a decision process with non-Markovian rewards (NMRDP) is to exploit a compact representation of the reward function to automatically translate the NMR…