FRCU - GIICIS: Grupo de Investigación en Inteligencia Computacional e Ingeniería de Software - Artículos

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/4095

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Author: search.filters.author.Schab, Esteban AlejandroStart date: 2020

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    Graph representations for reinforcement learning
    (Universidad Nacional de La Plata. Facultad de Informática., 2024-04) Schab, Esteban Alejandro; Casanova Pietroboni, Carlos Antonio; Piccoli, María Fabiana
    Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could ben- efit from advancements in graph analysis, given that a learning agent may be integrated into an environ- ment that can be represented as a graph. Nevertheless, the structural irregularity of graphs and the lack of prior labels make it difficult to integrate such a model into modern Reinforcement Learning frameworks that rely on artificial neural networks. Graph embedding enables the learning of low-dimensional vector representations that are more suited for machine learning algorithms, while retaining essential graph features. This paper presents a framework for evaluating graph embedding algorithms and their ability to preserve the structure and relevant features of graphs by means of an internal validation metric, without resorting to subsequent tasks that require labels for training. Based on this framework, three defined algorithms that meet the necessary requirements for solving a specific problem of Reinforcement Learningin graphs are selected, analyzed, and compared. These algorithms are Graph2Vec, GL2Vec, and Wavelet Characteristics, with the latter two demonstrating superior performance.
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    Hierarchical clustering-based framework for a posteriori exploration of pareto fronts : application on the bi-objective next release problem
    (Hector Florez, Universidad Distrital Francisco Jose de Caldas, Colombia., 2023-05-24) Casanova Pietroboni, Carlos Antonio; Schab, Esteban Alejandro; Prado, Lucas Martín; Rottoli, Giovani Daian
    When solving multi-objective combinatorial optimization problems using a search algorithm without a priori information, the result is a Pareto front. Selecting a solution from it is a laborious task if the number of solutions to be analyzed is large. This task would benefit from a systematic approach that facilitates the analysis, comparison and selection of a solution or a group of solutions based on the preferences of the decision makers. In the last decade, the research and development of algorithms for solving multi-objective combinatorial optimization problems has been growing steadily. In contrast, efforts in the a posteriori exploration of non-dominated solutions are still scarce.