no code implementations • 8 Apr 2024 • Dillon Z. Chen, Sylvie Thiébaux
Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception.
no code implementations • 25 Mar 2024 • Dillon Z. Chen, Felipe Trevizan, Sylvie Thiébaux
Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance.
no code implementations • 18 Dec 2023 • 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.
1 code implementation • 28 Sep 2023 • Alban Grastien, Patrik Haslum, Sylvie Thiébaux
This more general theory of diagnosis from first principles defines the minimal diagnosis as the set of preferred diagnosis candidates in a search space of hypotheses.
no code implementations • 25 Mar 2023 • 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 probabilistic planning modelled as a stochastic shortest path (SSP) problem.
no code implementations • 29 Nov 2019 • William Shen, Felipe Trevizan, Sylvie Thiébaux
We present the first approach capable of learning domain-independent planning heuristics entirely from scratch.
1 code implementation • 4 Aug 2019 • Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, Lexing Xie
In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets).
no code implementations • 19 Apr 2019 • Buser Say, Scott Sanner, Sylvie Thiébaux
We then strengthen the linear relaxation of the underlying MILP model by introducing constraints to bound the reward function based on the precomputed reward potentials.
1 code implementation • 13 Sep 2017 • Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, Lexing Xie
In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems.
no code implementations • 30 Jun 2017 • Peter Baumgartner, Sylvie Thiébaux, Felipe Trevizan
Policy synthesis addresses the problem of how to control or limit the decisions an agent makes so that a given specification is met.