# Automated planning with ontologies under coherence update semantics (Extended Version)

arXiv: https://arxiv.org/abs/2507.15120 · HTML: https://mlsanity.com/paper/2507.15120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [The DL-Lite Family and Relations](https://mlsanity.com/paper/1401.3487/) (2014)
- [Expressivity of Planning with Horn Description Logic Ontologies (Technical Report)](https://mlsanity.com/paper/2203.09361/) (2022)
- [Managing Change in Graph-structured Data Using Description Logics (long version with appendix)](https://mlsanity.com/paper/1404.4274/) (2014)

## Abstract (arXiv)

Standard automated planning employs first-order formulas under closed-world semantics to achieve a goal with a given set of actions from an initial state. We follow a line of research that aims to incorporate background knowledge into automated planning problems, for example, by means of ontologies, which are usually interpreted under open-world semantics. We present a new approach for planning with DL-Lite ontologies that combines the advantages of ontology-based action conditions provided by explicit-input knowledge and action bases (eKABs) and ontology-aware action effects under the coherence update semantics. We show that the complexity of the resulting formalism is not higher than that of previous approaches and provide an implementation via a polynomial compilation into classical planning. An evaluation of existing and new benchmarks examines the performance of a planning system on different variants of our compilation.