Bounded numeric planning, where each numeric variable domain is bounded, is PSPACE-
complete, but such a complexity result does not capture how hard it really is, since the same …

For combinatorial optimization problems, model-based approaches such as mixed-integer
programming (MIP) and constraint programming (CP) aim to decouple modeling and solving …

Graph learning is naturally well suited for use in symbolic, object-centric planning due to its
ability to exploit relational structures exhibited in planning domains and to take as input …

The LM-cut heuristic, both alone and as part of the operator counting framework, represents
one of the most successful heuristics for classical planning. In this paper, we generalize LM …

One of the main challenges of domain-independent numeric planning is the complexity of
the search problem. The exploitation of structural symmetries in a search problem can …

The use of planning techniques in traffic signal optimisation has proven effective in
managing unexpected traffic conditions as well as typical traffic patterns. However …

In this article, we present an overview of the 2023 International Planning Competition. It
featured five distinct tracks designed to assess cutting‐edge methods and explore the …

While numeric variables play an important, sometimes central, role in many planning
problems arising from real world scenarios, most of the currently available heuristic search …

In railway networks, stations are probably the most critical points for interconnecting trains'
routes: in a restricted geographical area, a potentially large number of trains have to stop …

This paper studies an approach to planning with PDDL3 constraints involving mixed
propositional and numeric conditions, as well as metric time constraints. We show how the …