Uso de minería de redes sociales para mejorar la interacción con un robot socialuna propuesta
- Castro-González, Álvaro 1
- Onorati, Teresa 1
- Morales Sánchez, Rodrigo 1
- Salichs, Miguel Ángel 1
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1
Universidad Carlos III de Madrid
info
- Carlos Balaguer Bernaldo de Quirós (coord.)
- José Manuel Andújar Márquez (coord.)
- Ramon Costa Castelló (coord.)
- Carlos Ocampo Martínez (coord.)
- Jesús Fernández Lozano (coord.)
- Matilde Santos Peñas (coord.)
- José Enrique Simó Ten (coord.)
- Montserrat Gil Martínez (coord.)
- Jose Luis Calvo Rolle (coord.)
- Raúl Marín Prades (coord.)
- Eduardo Rocón de Lima (coord.)
- Elisabet Estévez Estévez (coord.)
- Pedro Jesús Cabrera Santana (coord.)
- David Muñoz de la Peña Sequedo (coord.)
- José Luis Guzmán Sánchez (coord.)
- José Luis Pitarch Pérez (coord.)
- Oscar Reinoso García (coord.)
- Oscar Déniz Suárez (coord.)
- Emilio Jiménez Macías (coord.)
- Vanesa Loureiro Vázquez (coord.)
Editorial: Servizo de Publicacións ; Universidade da Coruña
ISBN: 978-84-9749-841-8
Año de publicación: 2022
Páginas: 654-661
Congreso: Jornadas de Automática (43. 2022. Logroño)
Tipo: Aportación congreso
Resumen
Currently, the interaction capabilities of social robots are limited and, especially in long-term interactions, the human-robot dialogues can be perceived as predictable, repetitive, and unnatural. This situation can lead the user to lose interest in the robot. If we want to bet on a successful and long-lasting coexistence, it is necessary to provide robots with more varied discourses that can adapt to the changing needs of the users. This contribution proposes a methodology that combines data mining and machine learning techniques to dynamically define the robot’s verbal communication through the content published on social networks. We propose to extract useful information from social networks to build knowledge models based on the context of the interaction so that it changes according to the published information.