规范多智能体系统中的多价值对齐：进化优化方法
Multi-Value Alignment in Normative Multi-Agent System: Evolutionary Optimisation Approach
AI总结：
针对多智能体系统中多价值对齐的缺失，提出基于多目标进化算法的多价值促进模型，生成最优规范参数集，并在多价值税收场景中验证了算法选择与价值关系的重要性。
摘要：
在规范多智能体系统中，价值对齐用于促进特定价值，并确保自主智能系统中智能体的行为与人类价值保持一致。然而，现有文献仅限于针对单一价值对齐的有效规范整合，未考虑智能体的异质性以及同时促进和对齐多个价值的需求。本研究提出了一种多价值促进模型，该模型使用多目标进化算法来生成最优参数化规范集，该规范集与异构智能体及系统的多个同时价值对齐。为了理解这一复杂问题的各个方面，本研究使用了多种进化算法来寻找一组优化的规范参数，并考虑了具有两个和五个价值的两个玩具税收场景。研究结果从不同角度进行了分析，以展示所选进化算法对解决方案的影响，以及在优先排序价值时理解价值之间关系的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
