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 …

The standard PDDL language for classical planning uses several first-order features, such
as schematic actions. Yet, most classical planners ground this first-order representation into …

Polynomial-time heuristic functions for planning are commonplace since 20 years. But
polynomial-time in which input? Almost all existing approaches are based on a grounded …

Automated planning is one of the foundational areas of AI. Since no single planner can work
well for all tasks and domains, portfolio-based techniques have become increasingly …

We study how group symmetry helps improve data efficiency and generalization for end-to-
end differentiable planning algorithms when symmetry appears in decision-making tasks …

Robots, in contrast to typical computational systems, affect the physical environment directly.
Therefore, other assumptions must be considered for task management procedures in these …

From the perspective of satisfaction, physical and mental health, and re-travel, this study
explored whether the development of religious and cultural tourism could construct a …

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 …

Classical planning tasks are modelled in PDDL which is a schematic language based on
first-order logic. Most of the current planners turn this lifted representation into a …

Classical planning tasks are usually modelled in the PDDL which is a schematic language
based on first-order logic. Nevertheless, most of the current planners turn this first-order …