# Norm Conflict Resolution in Stochastic Domains

arXiv: https://arxiv.org/abs/1706.07448 · HTML: https://mlsanity.com/paper/1706.07448/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2017.

## Built on by (most cited first, 7 of 7)

- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- [Reinforcement Learning Guided by Provable Normative Compliance](https://mlsanity.com/paper/2203.16275/) (2022)
- [Reinforcement Learning and Machine ethics:a systematic review](https://mlsanity.com/paper/2407.02425/) (2024)
- [Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models](https://mlsanity.com/paper/2506.11068/) (2025)
- [GRAIL: Goal Recognition Alignment through Imitation Learning](https://mlsanity.com/paper/2602.14252/) (2026)
- [HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning](https://mlsanity.com/paper/2505.15011/) (2025)
- [Deontically Constrained Policy Improvement in Reinforcement Learning Agents](https://mlsanity.com/paper/2506.06959/) (2025)

## Abstract (arXiv)

Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach to managing conflicting norms, which are common in human social interactions. Existing logic-based approaches suffer from normative explosion and are typically designed for deterministic environments; reward-based approaches lack principled ways of determining which normative alternatives exist in a given environment. We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity. We provide a proof-of-concept implementation in a simulated vacuum cleaning domain.