Automated planning with ontologies under coherence update semantics (Extended Version)
This work addresses the challenge of integrating open-world semantics from ontologies into automated planning, which is incremental as it builds on existing eKABs and coherence update semantics.
The paper tackles the problem of incorporating background knowledge into automated planning by introducing a new approach that combines ontology-based action conditions with coherence update semantics for DL-Lite ontologies, resulting in a formalism with no higher complexity than previous methods and an implementation via polynomial compilation into classical planning.
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.