Abstract 3D scene graphs (3DSGs) are an emerging description; unifying symbolic,
topological, and metric scene representations. However, typical 3DSGs contain hundreds of …

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 …

In imitation learning for planning, parameters of heuristic functions are optimized against a
set of solved problem instances. This work revisits the necessary and sufficient conditions of …

The algorithm selection problem is to choose the most suitable algorithm for solving a given
problem instance. It leverages the complementarity between different approaches that is …

As classical planning is known to be computationally hard, no single planner is expected to
work well across many planning domains. One solution to this problem is to use online …

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 transfer the notion of structural symmetries to lifted planning task representations, based
on abstract structures which we define to model planning tasks. We show that symmetries …

PLANUTILS is a general library for setting up Linux-based environments for developing,
running, and evaluating planners. Over the last decades, the planning community has …

We present a new algorithm called A*+ BFHS for solving problems with unit-cost operators
where A* and IDA* fail due to memory limitations and/or the existence of many distinct paths …

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 …