  State constraints in AI Planning globally restrict the legal environment
states. Standard planning languages make closed-domain and closed-world
assumptions. Here we address open-world state constraints formalized by
planning over a description logic (DL) ontology. Previously, this combination
of DL and planning has been investigated for the light-weight DL DL-Lite. Here
we propose a novel compilation scheme into standard PDDL with derived
predicates, which applies to more expressive DLs and is based on the
rewritability of DL queries into Datalog with stratified negation. We also
provide a new rewritability result for the DL Horn-ALCHOIQ, which allows us to
apply our compilation scheme to quite expressive ontologies. In contrast, we
show that in the slight extension Horn-SROIQ no such compilation is possible
unless the weak exponential hierarchy collapses. Finally, we show that our
approach can outperform previous work on existing benchmarks for planning with
DL ontologies, and is feasible on new benchmarks taking advantage of more
expressive ontologies. That is an extended version of a paper accepted at AAAI
22.
