Designing a planning domain is a difficult task in AI planning. Assisting tools are thus
required if we want planning to be used more broadly. In this paper, we are interested in …

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 …

Planning models are usually defined in lifted, ie first order formalisms, while most solvers
need (variable-free) grounded representations. Though techniques for grounding prune …

Automated planning is a form of declarative problem solving which has recently drawn
attention from the machine learning (ML) community. ML has been applied to planning …

While state-of-the-art planning systems need a grounded (propositional) task representation,
the input model is provided “lifted”, specifying predicates and action schemas with variables …

Classical planning instances are often represented using first-order logic; however, the
initial step for most classical planners is to transform the given instance into a propositional …

Most classical planners use grounding as a preprocessing step, essentially reducing
planning to propositional logic. However, grounding involves instantiating all rules and …

Heuristics for lifted planning are not yet as informed as the best heuristics for ground
planning. Recent work introduced the idea of using Datalog programs to compute the …

Current approaches for learning for planning have yet to achieve competitive performance
against classical planners in several domains, and have poor overall performance. In this …

Lifted planners are useful to solve tasks that are too hard to ground. Still, computing
informative lifted heuristics is difficult: directly adapting ground heuristics to the lifted setting …