To date, delete relaxation underlies some of the most effective heuristics for deterministic
planning. Despite its success, however, delete relaxation has significant pitfalls in many …

When engineering an automated planning model, domain authors typically assume a static,
unchanging ground-truth world. Unfortunately, this assumption can clash with reality, where …

The International Planning Competition (IPC) is a prominent event of the artificial
intelligence planning community that has been organized since 1998; it aims at fostering the …

We investigate learning heuristics for domain-specific planning. Prior work framed learning a
heuristic as an ordinary regression problem. However, in a greedy best-first search, the …

LAMA-2011 (Richter and Westphal 2010) is the clear winner of the sequential satisficing
track in the latest International Planning Competition (IPC-2011). It finds a first solution by …

Testing is a promising way to gain trust in neural action policies π. Previous work on policy
testing in sequential decision making targeted environment behavior leading to failure …

In this paper we study the computational complexity of several reasoning tasks centered
around the bounded plan existence problem. We do this for standard classical planning and …

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 …

Abstract Property Directed Reachability (PDR) is a very promising recent method for
deciding reachability in symbolically represented transition systems. While originally …

Even though automated planning is PSPACE-complete in general, satisficing planners are
able to solve large planning tasks quickly. However, the found plans are often far from …