The majority of multi-agent system implementations aim to optimise agents’ policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems.

中文翻译：

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多目标多主体决策：基于效用的分析和调查

尽管许多现实世界中的问题域本质上本质上都是多目标的，但大多数多代理系统实现都是针对单个目标优化代理策略。多目标多主体系统（MOMAS）明确考虑了冲突目标函数之间的可能权衡。我们认为，在MOMAS中，应基于这些妥协对系统用户的实用性来分析此类妥协。按照多目标优化的标准，我们使用效用函数对用户效用进行建模，这些效用函数将值或返回向量映射到标量值。这种方法自然会导致两个不同的优化标准：预期的标量回报（ESR）和标量的预期回报（SER）。我们开发了一种新的分类法，该分类法根据奖励结构以及效用函数的应用方式和用途，对多目标多主体决策设置进行分类。这使我们能够提供该领域的结构化视图，以清晰地描绘出当前多目标多主体决策方法中的最新技术，并为未来的研究确定有希望的方向。从执行阶段开始，在该阶段将应用选定的策略并获得针对用户的实用程序，我们将分析哪些解决方案概念适用于分类法中的不同设置。此外，我们在ESR和SER优化标准下定义和讨论这些解决方案概念。