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

TL;DR

This paper introduces a multi-value promotion model using multi-objective evolutionary algorithms to optimize norms in heterogeneous multi-agent systems, addressing the challenge of aligning multiple values simultaneously.

Contribution

It proposes a novel multi-value alignment approach with evolutionary algorithms, considering agent heterogeneity and multiple values, which was not addressed in prior work.

Findings

Different evolutionary algorithms impact solution quality.

Understanding value relations influences prioritization.

Optimized norms improve multi-value alignment.

Abstract

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.