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

TL;DR

This paper introduces a novel approach to automated planning that integrates DL-Lite ontologies with coherence update semantics, enabling ontology-aware action effects while maintaining manageable complexity and providing a polynomial compilation into classical planning.

Contribution

It presents a new formalism for planning with DL-Lite ontologies combining explicit-input knowledge and ontology-aware effects, with an efficient compilation method and empirical evaluation.

Findings

Complexity remains comparable to previous approaches.

Successful polynomial compilation into classical planning.

Evaluation shows competitive performance on various benchmarks.

Abstract

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.