# Multi-Value Alignment in Normative Multi-Agent System: Evolutionary Optimisation Approach

arXiv: https://arxiv.org/abs/2305.07366 · HTML: https://mlsanity.com/paper/2305.07366/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 4 of 4)

- [Learning Norms from Stories: A Prior for Value Aligned Agents](https://mlsanity.com/paper/1912.03553/) (2019)
- [Value alignment: a formal approach](https://mlsanity.com/paper/2110.09240/) (2021)
- [Improved Quick Hypervolume Algorithm](https://mlsanity.com/paper/1612.03402/) (2016)
- [Run-time Norms Synthesis in Multi-Objective Multi-Agent Systems](https://mlsanity.com/paper/2105.00124/) (2021)

## Abstract (arXiv)

Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behavior of agents in autonomous intelligent systems with human values. However, the current literature is limited to incorporation of effective norms for single value alignment with no consideration of agents' heterogeneity and the requirement of simultaneous promotion and alignment of multiple values. This research proposes a multi-value promotion model that uses multi-objective evolutionary algorithms to produce the optimum parametric set of norms that is aligned with multiple simultaneous values of heterogeneous agents and the system. To understand various aspects of this complex problem, several evolutionary algorithms were used to find a set of optimised norm parameters considering two toy tax scenarios with two and five values are considered. The results are analysed from different perspectives to show the impact of a selected evolutionary algorithm on the solution, and the importance of understanding the relation between values when prioritising them.