Artificial semantic memory with autonomous learning applied to social robots
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Autores: Martín Rico Ortega, Francisco Gómez Donoso
, Félix Escalona Moncholi , Miguel Cazorla Quevedo , José García Rodríguez
- Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.) , José Ramón Álvarez Sánchez (dir. congr.) , Félix de la Paz López (dir. congr.) , Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 401-411
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Semantic memory stores knowledge about the meanings ofwords and the relationships between these meanings. In recent years, Artificial Intelligence, in particular Deep Learning, has successfully resolved the identification of classes of elements in images, and even instances of a class, providing a basic form of semantic memory. Unfortunately,incorporating new instances of a class requires a complex andlong process of labeling and offline training. We are convinced that the combination of convolutional networks and statistical classifiers allows us to create a long-term semantic memory that is capable of learning online. To validate this hypothesis, we have implemented a long-term semantic memory in a social robot. The robot initially only recognizes people, but, after interacting with different people, is able to distinguish them from each other. The advantage of our approach is that the process of long-term memorization is done autonomously without the need foroffline processing.
