Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems
Nathalia Nascimento Paulo Alencar Donald Cowan
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.