Principles2Plan: LLM-Guided System for Operationalising Ethical Principles into Plans
Zhong, Tammy, Song, Yang, Pagnucco, Maurice
–arXiv.org Artificial Intelligence
Ethical awareness is critical for robots operating in human environments, yet existing automated planning tools provide little support. Manually specifying ethical rules is labour-intensive and highly context-specific. We present Princi-ples2Plan, an interactive research prototype demonstrating how a human and a Large Language Model (LLM) can collaborate to produce context-sensitive ethical rules and guide automated planning. A domain expert provides the planning domain, problem details, and relevant high-level principles such as beneficence and privacy. The system generates op-erationalisable ethical rules consistent with these principles, which the user can review, prioritise, and supply to a planner to produce ethically-informed plans. To our knowledge, no prior system supports users in generating principle-grounded rules for classical planning contexts. Principles2Plan showcases the potential of human-LLM collaboration for making ethical automated planning more practical and feasible.
arXiv.org Artificial Intelligence
Dec-10-2025
- Country:
- Europe > France
- Occitanie > Hérault > Montpellier (0.04)
- Oceania > Australia
- New South Wales > Sydney (0.04)
- Europe > France
- Genre:
- Research Report (0.40)
- Technology: