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Found 10 papers, 3 papers with code
Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning
Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception.
Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning
Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance.
Learning Domain-Independent Heuristics for Grounded and Lifted Planning
We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search.
A More General Theory of Diagnosis from First Principles
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.
Heuristic Search for Multi-Objective Probabilistic Planning
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.
Learning Domain-Independent Planning Heuristics with Hypergraph Networks
We present the first approach capable of learning domain-independent planning heuristics entirely from scratch.
ASNets: Deep Learning for Generalised Planning
In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets).
Reward Potentials for Planning with Learned Neural Network Transition Models
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.
Action Schema Networks: Generalised Policies with Deep Learning
In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems.
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Policy synthesis addresses the problem of how to control or limit the decisions an agent makes so that a given specification is met.
