Máster en Intelixencia Artificial

Permanent URI for this collectionhttps://hdl.handle.net/10347/37706

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    Towards Autonomous Web Navigation with LLM-based Agents
    (2025-02-21) Izquierdo Álvarez, Mario; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Morón Reyes, Francisco José; Fernández Pichel, Marcos
    Autonomous web navigation remains a complex challenge, particularly due to the dynamic, diverse and unstructured nature of web environments. Traditional web scraping techniques, while effective, require rigid configurations tied to specific website structures, limiting their generalizability. To address these challenges, this work explores the usage of autonomous agents powered by Large Language Models for autonomous web navigation, focusing on the retrieval of academic publications from webs of preprint repositories. The proposed solution, based on hyperlink exploration, is designed as a component of a potentially broader system for AI-driven paper search assistance. It leverages a multi-agent architecture and a structured tree-traversal like approach to explore and extract relevant documents. Each agent is assigned a specific role, including relevant URL extraction, document collection, planning, presentation and quality control. The system is implemented using AutoGen, which enables flexible agent interactions and modular design. Unlike traditional web information extraction techniques, this approach generalizes navigation patterns across different websites without relying on predefined HTML selectors, allowing its usage on different websites. Experimental results are promising, demonstrating the system’s effectiveness in retrieving relevant academic content. However, challenges such as increased response times and occasional hallucinations indicate areas for refinement. Future work aims to enhance interactivity by integrating advanced form-based search capabilities, optimize retrieval efficiency, and implement more robust evaluation frameworks. These improvements could contribute to fully automated AI-driven web exploration, facilitating the development of more generalizable autonomous web navigation tools.
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    Rule extraction for process mining based on machine learning techniques
    (2024-02-06) Benavides Álvarez, Tomás; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Mucientes Molina, Manuel; Lama Penín, Manuel
    Process mining is a discipline that has been gaining importance by offering a set of techniques that allow extracting knowledge from the event logs in which the information generated in the execution of processes is stored. One of the main objectives in process mining is to understand what has happened during the execution of a process. Typically, this goal is achieved by manually exploring the actual model, describing the behaviour of the process and temporal and frequency analytics on its variants and business indicators. In this paper, an innovative approach based on decision trees is presented that allows the automatic classification of certain behaviours that occur during a process based on the information generated during its executions and the variables associated with them, so that process stakeholders can have a better understanding of what is going on and thus improve decision making. This technique has been validated on a medical process, the Aortic Stenosis Integrated Care Process (AS ICP) implemented in the Cardiology Department of the University Hospital of Santiago de Compostela. On this process, the waiting times of patients have been tackled in order to extract those patient profiles susceptible to delays or to be prioritised.