Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally
computing abstractions of transition systems. We propose a CEGAR algorithm for computing …

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 …

Polynomial-time heuristic functions for planning are commonplace since 20 years. But
polynomial-time in which input? Almost all existing approaches are based on a grounded …

How can we train neural network (NN) heuristic functions for classical planning, using only
states as the NN input? Prior work addressed this question by (a) per-instance imitation …

Planning problems of practical relevance commonly include multiple objectives that are
difficult to weight a priori. Several heuristic search algorithms computing the Pareto front of …

Width-based planning methods deal with conjunctive goals by decomposing problems into
subproblems of low width. Algorithms like SIW thus fail when the goal is not easily …

Width-based planning methods deal with conjunctive goals by decomposing problems into
subproblems of low width. Algorithms like SIW thus fail when the goal is not easily …

Abstraction heuristics are the state of the art in optimal classical planning as heuristic
search. A popular method for computing abstractions is the counterexample-guided …

Automated problem reformulation is a common technique in classical planning to identify
and exploit problem structures. Decoupled search is an approach that automatically …

We describe a new algorithm for generating pattern collections for pattern database
heuristics in optimal classical planning. The algorithm uses the counterexample-guided …