Automated planning with ontologies under coherence update semantics

2025-07-20

AI Summary

This paper addresses the challenge of integrating background knowledge and action models in ontology-aware automated planning. We propose a novel planning framework grounded in DL-Lite ontologies and consistency-updating semantics. The framework uniformly models action preconditions and effects from explicit Knowledge Base (eKAB) specifications alongside ontological constraints, ensuring logically consistent state evolution while preserving the open-world assumption. Our key contribution is a polynomial-time compilation algorithm that reduces ontology-aware planning problems to classical planning instances, guaranteeing computational complexity no higher than that of standard classical planners. We empirically validate the efficiency and scalability of our approach on both established and newly constructed benchmarks. Furthermore, we implement a fully functional prototype system demonstrating practical applicability.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Incorporating ontologies into automated planning problems
• Combining ontology-based action conditions and effects
• Evaluating complexity and performance of new planning approach

Innovation

• DL-Lite ontologies for automated planning
• Coherence update semantics for action effects
• Polynomial compilation into classical planning