arXiv:2601.19644v2 Announce Type: replace-cross Abstract: Decidability or complexity issues about the consistency problem for description logics with concrete domains have already been analysed with tableaux-based or type elimination methods. Concrete domains in ontologies are essential to consider concrete objects and predefined relations. In this work, we expose an automata-based approach leading to the optimal upper bound EXPTIME, that is designed by enriching the transitions with symbolic constraints. We show that the nonemptiness problem for such automata belongs to EXPTIME if the concret
Ongoing research in AI and formal methods continues to seek more efficient and robust solutions for knowledge representation and reasoning, especially as AI systems become more complex and require explainability.
Improving the robustness and efficiency of foundational AI reasoning methods like description logics with concrete domains enhances the reliability and scalability of advanced AI applications, particularly in areas requiring precise constraint handling.
This research introduces an automata-based approach with potentially optimal complexity for a critical inference problem in description logics, offering a new, more efficient tool for consistency checking in complex ontological systems.
- · AI researchers and developers
- · Knowledge representation systems
- · Industries relying on formal verification
- · Less efficient tableaux-based methods
- · Systems with high computational overhead
More efficient tools for handling concrete domains in description logics will reduce computational costs in certain AI applications.
This could enable the deployment of more complex and robust AI systems in domains like manufacturing, cybersecurity, or legal tech where detailed constraint satisfaction is crucial.
Long-term, improved foundational reasoning could contribute to the development of more trustworthy and explainable AI agents, impacting their broader societal adoption.
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Read at arXiv cs.AI