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 …

The merge-and-shrink framework has been introduced as a general approach for defining
abstractions of large state spaces arising in domain-independent planning and related …

Cost-optimal planning has not seen many successful approaches that work well across all
domains. Some costoptimal planners excel on some domains, while exhibiting less exciting …

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 …

Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has
motivated AI researchers to explore efficient alternative representations and problem-solving …

Classical planning tasks are modelled in PDDL which is a schematic language based on
first-order logic. Most of the current planners turn this lifted representation into a …

We present a new algorithm called A*+ BFHS for solving problems with unit-cost operators
where A* and IDA* fail due to memory limitations and/or the existence of many distinct paths …

Merge-and-shrink heuristics are a successful class of abstraction heuristics used for optimal
classical planning. With the recent addition of generalized label reduction, merge-and-shrink …

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 …

Abstract Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of
states, is a competitive approach to optimal planning. Yet heuristic search in this context …