Percorrer por autor "OLIVEIRA, FRANCISCO LUÍS PEREIRA"
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- ARMS: Augmented Reasoning Multi-Agent SystemPublication . OLIVEIRA, FRANCISCO LUÍS PEREIRA; Gomes, Luís Filipe de OliveiraThe developments brought by the transformer architecture have sparked a technological revolution that created a wide range of possible use cases where these models are employed as individuals responsible for handling a diverse array of tasks, from chatbots to more deterministic such as API call and control. However, due to the novelty of these models there has been a lack for standardization when developing proper, controlled real implementations. It is assumed that the present time is considered to be an alchemy-like stage of large-language model usage, and many dierent innovations are born almost every day and everywhere around the world. Great investments are also being made on the field, and there has never been better time to dedicate eorts into discovering and exploring the limits and capacities of this technology of the future. Multi-agent systems belong to a domain of artificial intelligence that has been in the development for many years resulting in refined and mature architectures, communication protocols, and implementation paradigms. However, implementation might sometimes be diicult due to the overhead required in orchestrating proper communication protocols, decision engines, and agent architecture. Furthermore, agent-to-human communication is not always seamless since most agents have programmatic-machine language which might not be easy for actors that are not contextualized or are technically inclined to interact with. This dissertation proposes a system that aims to fuse the capabilities of large-language models to communicate through natural language and rationalize inputs with the capabilities that distributed multi-agent systems oer to resolve tasks that might be present in industrial and smart-building scenarios. Moreover, through the implementation of specific pieces of hardware, referred below as tools, the proposed system tries to increase the degree of impact that decisions made by large-language models have in the environment around them. The system proposed, named “Augmented Reasoning Multi-Agent System” (ARMS), also allows users to communicate directly with agents through natural language conversations facilitating information and desire exchange. Agent-to-Agent communication is also deeply investigated and controlled using specific techniques to manage communication flow and objective-oriented exchanges. Besides a review of the state-of-the-art on topics related to the solution that culminates in a discussion about large-language model-powered agents vs traditional agents, this thesis includes five dierent that test the solution: basic task delegation, interconnected agents, user registration system, vacation system, and building control. Each of these case studies were built incrementally, meaning that the most basic and core principles were firstly tested on the first use cases, culminating on a final one that integrated multiple components previously tested at a large scale. The results from the case studies demonstrated positive results in achieving a multi-agent system that can manipulate the world around it and establish human communication as needed, leveraging large-language models’ capabilities for the decision-making processes, as well as inter-connection.