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 …

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 …

A pattern database (PDB) for a planning task is a heuristic function in the form of a lookup
table that contains optimal solution costs of a simplified version of the task. In this paper we …

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

Cost partitioning is a method for admissibly adding multiple heuristics for state-space
search. Saturated cost partitioning considers the given heuristics in sequence, assigning to …

Cost partitioning is a general method for admissibly summing up heuristic estimates for
optimal state-space search. Most cost partitioning algorithms can optimize the resulting cost …

Cost partitioning is a general method for adding multiple heuristic values admissibly. In the
setting of optimal classical planning, saturated cost partitioning has recently been shown to …

Pattern databases (PDBs) are memory-based abstraction heuristics that are constructed
prior to the planning process which, if expressed symbolically, yield a very efficient …

Cost partitioning is a method for admissibly combining admissible heuristics. In this work, we
extend this concept to merge-and-shrink (M&S) abstractions that may use labels that do not …