  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.
