Expressivity of Planning with Horn Description Logic Ontologies

Stefan BorgwardtStefan Borgwardt, Jörg HoffmannJörg Hoffmann, Alisa KovtunovaAlisa Kovtunova, Markus KrötzschMarkus Krötzsch, Bernhard NebelBernhard Nebel, Marcel SteinmetzMarcel Steinmetz

Download

Stefan Borgwardt, Jörg Hoffmann, Alisa Kovtunova, Markus Krötzsch, Bernhard Nebel, Marcel Steinmetz
Expressivity of Planning with Horn Description Logic Ontologies
Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 5503--5511, 2022. AAAI Press

Details
Bibtex

KurzfassungAbstract
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.
Projekt:Project: CPEC
Forschungsgruppe:Research Group: AutomatentheorieAutomata Theory, Wissensbasierte SystemeKnowledge-Based Systems

@inproceedings{BHKKNS2022,
  author    = {Stefan Borgwardt and J{\"{o}}rg Hoffmann and Alisa Kovtunova and
               Markus Kr{\"{o}}tzsch and Bernhard Nebel and Marcel Steinmetz},
  title     = {Expressivity of Planning with Horn Description Logic Ontologies},
  booktitle = {Proceedings of the 36th {AAAI} Conference on Artificial
               Intelligence (AAAI 2022)},
  publisher = {AAAI Press},
  year      = {2022},
  pages     = {5503--5511}
}