Multi-agent system

[3] With advancements in Large language model (LLMs), LLM-based multi-agent systems have emerged as a new area of research, enabling more sophisticated interactions and coordination among agents.

The goal of an ABM is to search for explanatory insight into the collective behavior of agents (which do not necessarily need to be "intelligent") obeying simple rules, typically in natural systems, rather than in solving specific practical or engineering problems.

[5] Applications where multi-agent systems research may deliver an appropriate approach include online trading,[6] disaster response,[7][8] target surveillance[9] and social structure modelling.

This middleware offers a first-class design abstraction for multi-agent systems, providing means to govern resource access and agent coordination.

[citation needed] When agents can share knowledge using any agreed language, within the constraints of the system's communication protocol, the approach may lead to a common improvement.

Other applications[29] include transportation,[30] logistics,[31] graphics, manufacturing, power system,[32] smartgrids,[33] and the GIS.

[34] Vehicular traffic with controlled autonomous vehicles can be modelling as a multi-agent system involving crowd dynamics.

[35] Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents.

[36] Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars.