Expressivity of Planning with Horn Description Logic Ontologies (Technical Report)

TL;DR

This paper introduces a novel compilation method for planning with expressive description logic ontologies into standard PDDL, enabling more effective planning in open-world scenarios and outperforming previous approaches on benchmarks.

Contribution

It presents a new compilation scheme for expressive DL ontologies into PDDL, leveraging DL query rewritability, and demonstrates its applicability to more expressive DLs like Horn-ALCHOIQ.

Findings

Applicable to expressive DLs like Horn-ALCHOIQ

Outperforms previous planning approaches on benchmarks

Feasible with more expressive ontologies

Abstract

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.