Autonomous agents driven by Large Language Models (LLMs) offer enormous
potential for automation. Early proof of this technology can be found in
various demonstrations of agents solving complex tasks, interacting with
external systems to augment their knowledge, and triggering actions. In
particular, workflows involving multiple agents solving complex tasks in a
collaborative fashion exemplify their capacity to operate in less strict and
less well-defined environments. Thus, a multi-agent approach has great
potential for serving as a backbone in many industrial applications, ranging
from complex knowledge retrieval systems to next generation robotic process
automation. Given the reasoning abilities within the current generation of
LLMs, complex processes require a multi-step approach that includes a plan of
well-defined and modular tasks. Depending on the level of complexity, these
tasks can be executed either by a single agent or a group of agents. In this
work, we focus on designing a flexible agent engineering framework with careful
attention to planning and execution, capable of handling complex use case
applications across various domains. The proposed framework provides
reliability in industrial applications and presents techniques to ensure a
scalable, flexible, and collaborative workflow for multiple autonomous agents
working together towards solving tasks.

设计了一个灵活的智能体工程框架，着重规划和执行，适用于多个领域的复杂应用，提供可靠性的工业应用，并提出了确保多个自主智能体共同解决任务的可扩展、灵活和协作式工作流技术。

BMW Agents - 多智能体协作的任务自动化框架

BMW Agents -- A Framework For Task Automation Through Multi-agent  Collaboration

This paper introduces a full solution for decentralized routing in Low Earth
Orbit satellite constellations based on continual Deep Reinforcement Learning
(DRL). This requires addressing multiple challenges, including the partial
knowledge at the satellites and their continuous movement, and the time-varying
sources of uncertainty in the system, such as traffic, communication links, or
communication buffers. We follow a multi-agent approach, where each satellite
acts as an independent decision-making agent, while acquiring a limited
knowledge of the environment based on the feedback received from the nearby
agents. The solution is divided into two phases. First, an offline learning
phase relies on decentralized decisions and a global Deep Neural Network (DNN)
trained with global experiences. Then, the online phase with local, on-board,
and pre-trained DNNs requires continual learning to evolve with the
environment, which can be done in two different ways: (1) Model anticipation,
where the predictable conditions of the constellation are exploited by each
satellite sharing local model with the next satellite; and (2) Federated
Learning (FL), where each agent's model is merged first at the cluster level
and then aggregated in a global Parameter Server. The results show that,
without high congestion, the proposed Multi-Agent DRL framework achieves the
same E2E performance as a shortest-path solution, but the latter assumes
intensive communication overhead for real-time network-wise knowledge of the
system at a centralized node, whereas ours only requires limited feedback
exchange among first neighbour satellites. Importantly, our solution adapts
well to congestion conditions and exploits less loaded paths. Moreover, the
divergence of models over time is easily tackled by the synergy between
anticipation, applied in short-term alignment, and FL, utilized for long-term
alignment.

这篇论文介绍了基于持续的深度强化学习的低轨卫星星座分散路由的完整解决方案，采用多智能体方法，其中每个卫星作为一个独立的决策制定智能体，通过从附近的智能体接收的反馈来获取环境的有限知识。

连续深度强化学习在分散卫星路由中的应用

Continual Deep Reinforcement Learning for Decentralized Satellite  Routing

In this paper, we are concerned with the automated exchange of orders between
logistics companies in a marketplace platform to optimize total revenues. We
introduce a novel multi-agent approach to this problem, focusing on the
Collaborative Vehicle Routing Problem (CVRP) through the lens of individual
rationality. Our proposed algorithm applies the principles of Vehicle Routing
Problem (VRP) to pairs of vehicles from different logistics companies,
optimizing the overall routes while considering standard VRP constraints plus
individual rationality constraints. By facilitating cooperation among competing
logistics agents through a Give-and-Take approach, we show that it is possible
to reduce travel distance and increase operational efficiency system-wide. More
importantly, our approach ensures individual rationality and faster
convergence, which are important properties of ensuring the long-term
sustainability of the marketplace platform. We demonstrate the efficacy of our
approach through extensive experiments using real-world test data from major
logistics companies. The results reveal our algorithm's ability to rapidly
identify numerous optimal solutions, underscoring its practical applicability
and potential to transform the logistics industry.

我们提出了一种创新的多智能体方法，聚焦于合作车辆路径问题，通过驾驶员个体有理性约束来优化物流公司间市场平台上的订单交换，实现降低行驶距离、提高系统运营效率，并确保市场长期可持续性。