Unavoidable dead-ends are common in many probabilistic planning problems, eg when
actions may fail or when operating under resource constraints. An important objective in …

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 …

In many usage scenarios of AI Planning technology, users will want not just a plan π but an
explanation of the space of possible plans, justifying π. In particular, in oversubscription …

Sampling efficiency in a highly constrained environment has long been a major challenge
for sampling-based planners. In this work, we propose Rapidly-exploring Random disjointed …

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 …

The use of multiagent systems (MASs) in real-world applications keeps increasing, and
diffuses into new domains, thanks to technological advances, increased acceptance, and …

Research on heuristic functions is all about estimating the length (or cost) of solution paths.
But what if there is no such path? Many known heuristics have the ability to detect (some) …

Sampling-based motion planners perform exceptionallywell in robotic applications that
operate in high-dimensional spaces. However, most previous works often constrain 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 …

Justifying a plan to a user requires answering questions about the space of possible plans.
Recent work introduced a framework for doing so via planproperty dependencies, where …