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 …

Numeric planning is known to be undecidable even under severe restrictions. Prior work has
investigated the decidability boundaries by restricting the expressiveness of the planning …

Dynamic programming (DP) is a framework used in multiple disciplines to solve decision-
making problems. In particular, in computer science and operations research (OR), DP …

In this paper, we first define numeric additive planning (NAP), a planning formulation
equivalent to Hoffmann's Restricted Tasks over Integers. Then, we analyze the minimal …

In numeric AI planning, a state is represented by propositions and numeric variables, actions
change the values of numeric variables in addition to adding and deleting propositions, and …

Several multi-agent techniques are utilized to reduce the complexity of classical planning
tasks, however, their applicability to temporal planning domains is a currently open line of …

It is possible for agents operating in a shared environment to interfere with one another. One
mechanism of coordination is called Social Law. Enacting such a law in a multi-agent setting …

Heuristic search is a powerful approach for solving planning problems and numeric
planning is no exception. In this paper, we boost the performance of heuristic search for …

With the completion of this thesis, I have fulfilled the final requirement to obtain my master's
degree in Embedded Systems at Delft University of Technology. In this thesis, I explore the …

NLM-CutPlan is a numeric planner based on heuristic search using the numeric LM-cut
heuristic. For simple numeric planning, it has a specific configuration using an …