As a unifying concept in economics, game theory, and operations research, even in the Robotics and AI field, the utility is used to evaluate the level of individual needs, preferences, and interests. Especially for decision-making and learning in multi-agent/robot systems (MAS/MRS), a suitable utility model can guide agents in choosing reasonable strategies to achieve their current needs and learning to cooperate and organize their behaviors, optimizing the system's utility, building stable and reliable relationships, and guaranteeing each group member's sustainable development, similar to the human society. Although these systems' complex, large-scale, and long-term behaviors are strongly determined by the fundamental characteristics of the underlying relationships, there has been less discussion on the theoretical aspects of mechanisms and the fields of applications in Robotics and AI. This paper introduces a utility-orient needs paradigm to describe and evaluate inter and outer relationships among agents' interactions. Then, we survey existing literature in relevant fields to support it and propose several promising research directions along with some open problems deemed necessary for further investigations.

翻译：作为经济学、博弈论和运筹学中的一个统一概念，效用甚至被应用于机器人学与人工智能领域，用以评估个体需求、偏好和兴趣的层次。特别是在多智能体/多机器人系统（MAS/MRS）的决策与学习中，合适的效用模型能够引导智能体选择合理策略以满足当前需求，并学会协作和组织自身行为，从而优化系统效用、构建稳定可靠的关系，并保障每个团体成员的可持续发展，这与人类社会相似。尽管这些系统的复杂、大规模和长期行为在很大程度上取决于底层关系的基本特征，但对于机器人学与人工智能中机制的理论方面及应用领域的讨论却较少。本文引入了一种面向效用的需求范式，以描述和评估智能体交互中的内部与外部关系。随后，我们调查了相关领域的现有文献以支持这一范式，并提出了一些有前景的研究方向，以及若干值得深入探讨的开放性问题。