In imitation learning for planning, parameters of heuristic functions are optimized against a
set of solved problem instances. This work revisits the necessary and sufficient conditions of …

Cost partitioning is a method for admissibly combining a set of admissible heuristic
estimators by distributing operator costs among the heuristics. Computing an optimal cost …

Generalized planning aims at computing solutions that work for all instances of the same
domain. In this paper, we show that several interesting planning domains possess compact …

In this paper, we focus on the inference of mutex groups in the lifted (PDDL) representation.
We formalize the inference and prove that the most commonly used translator from the Fast …

We analyze how complex a heuristic function must be to directly guide a state-space search
algorithm towards the goal. As a case study, we examine functions that evaluate states with …

Automated hyperparameter optimization (HPO) has gained great popularity and is an
important component of most automated machine learning frameworks. However, the …

Conflict-directed learning is ubiquitous in constraint satisfaction problems like SAT, but has
been elusive for state space search on reachability problems like classical planning. Almost …

Simplifying classical planning tasks by removing operators while preserving at least one
optimal solution can significantly enhance the performance of planners. In this paper, we …

In classical planning, cost partitioning is a method for admissibly combining a set of heuristic
estimators by distributing operator costs among the heuristics. An optimal cost partitioning is …

Potential heuristics for state-space search are defined as weighted sums over simple state
features. Atomic features consider the value of a single state variable in a factored state …