# Norm Conflict Resolution in Stochastic Domains

## TL;DR

This paper introduces a hybrid method combining Linear Temporal Logic with Markov Decision Processes to systematically resolve conflicting social and moral norms in stochastic environments, demonstrated through a simulated cleaning task.

## Contribution

It presents a novel hybrid approach that manages norm conflicts in stochastic domains using LTL and MDPs, addressing limitations of existing logic-based and reward-based methods.

## Key findings

- Successfully manages conflicting norms in a stochastic environment
- Provides a systematic framework for norm conflict resolution
- Demonstrates effectiveness in a simulated vacuum cleaning domain

## Abstract

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.