Expressivity of Planning with Horn Description Logic Ontologies (Technical Report)
This work addresses the challenge of open-world state constraints in planning for AI researchers, offering a method to handle more expressive ontologies, though it is incremental as it builds on prior DL and planning combinations.
The paper tackles the problem of integrating expressive description logic ontologies into AI planning by proposing a novel compilation scheme into standard PDDL, enabling planning with more expressive DLs like Horn-ALCHOIQ and showing improved performance on benchmarks.
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.