In autonomic computing, self-adaptation has been proposed as a fundamental paradigm to manage the complexity of multiagent systems (MASs). This achieved by extending a system with support to monitor and adapt itself to achieve specific concerns of interest. Communication in these systems is key given that in scenarios involving agent interaction, it enhances cooperation and reduces coordination challenges by enabling direct, clear information exchange. However, improving the expressiveness of the interaction communication with MASs is not without challenges. In this sense, the interplay between self-adaptive systems and effective communication is crucial for future MAS advancements. In this paper, we propose the integration of large language models (LLMs) such as GPT-based technologies into multiagent systems. We anchor our methodology on the MAPE-K model, which is renowned for its robust support in monitoring, analyzing, planning, and executing system adaptations in response to dynamic environments. We also present a practical illustration of the proposed approach, in which we implement and assess a basic MAS-based application. The approach significantly advances the state-of-the-art of self-adaptive systems by proposing a new paradigm for MAS self-adaptation of autonomous systems based on LLM capabilities.

翻译：在自主计算中，自适应已被提出作为管理多智能体系统（MAS）复杂性的基本范式。这一范式通过扩展系统以支持监控和自适应能力，从而实现对特定关注目标的响应。在这些系统中，通信至关重要——在涉及智能体交互的场景中，直接、清晰的信息交换能够增强协作、减少协调挑战。然而，提升MAS交互通信的表达能力并非没有挑战。在此意义上，自适应系统与有效通信之间的相互作用对MAS的未来发展至关重要。本文提出将大型语言模型（LLM）技术（如基于GPT的技术）集成到多智能体系统中。我们以MAPE-K模型为基础方法，该模型以其对动态环境下系统监控、分析、规划与执行适应的强健支持而闻名。同时，我们通过实际应用案例展示了所提方法：实现并评估了一个基于MAS的基础应用。该方法通过提出基于LLM能力的自主系统MAS自适应新范式，显著推进了自适应系统的前沿研究。