Axioms are an extension for classical planning models that allow for modeling complex preconditions and goals exponentially more compactly. Although axioms were introduced in planning more than a decade ago, modern planning techniques rarely support axioms, especially in cost-optimal planning. Symbolic search is a popular and competitive optimal planning technique based on the manipulation of sets of states. In this work, we extend symbolic search algorithms to support axioms natively. We analyze different ways of encoding derived variables and axiom rules to evaluate them in a symbolic representation. We prove that all encodings are sound and complete, and empirically show that the presented approach outperforms the previous state of the art in costoptimal classical planning with axioms.This work was supported by the German National Science Foundation (DFG) as part of the project EPSDAC (MA 7790/1-1) and the Research Unit FOR 1513 (HYBRIS). The FAI group of Saarland University has received support by DFG grant 389792660 as part of TRR 248 (see https://perspicuous-computing.science)
