2025/07/20 by Stefan Borgwardt, Borgwardt, Stefan, Duy Nhu +3
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Multi-Agent Systems and Negotiation #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2507.15120
openalex publication_date 2025/07/20 · openalex created_date 2025/10/07 · openalex updated_date 2026/07/28
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.