Multi-Agent System Used for Recommendation of Historical and Cultural Memories

Nancy Gelves

Abstract


 In this document the proposal of a recommendation system based on multi agent is made allowing the analysis of user behavior when visiting historical and cultural memories, giving recommendations based on qualifications and duration times for the observation of art pieces. It is also possible to see the system architecture, the server used for the development of the multi-agent system, as well as the communication between agents to carry out a route, and the functionality for recommending new routes to a user. The multi-agent system uses a neural network that allows to analyze the behavior of a user in a route; using the feedback given for the neural network the data is checked, allowing determine the user preferences. A set of historical and cultural memory data set is used to generate recommendations; in addition a user storage api is employed. For the system visualization, this prototype is connected with an augmented reality application that allows users access to visit art pieces and use predefined preferences.


References


P. Resnick and H. R. Varian, “Recommender systems”, Commun. ACM, vol. 40, no. 3, pp. 56–58, Mar. 1997.

I. Benouaret and D. Lenne, “Personalizing the Museum Experience through Context-Aware Recommendations", Proc. 2015 IEEE Int. Conf. Syst. Man, Cybern. SMC 2015, pp. 743-748, 2016.

L. F. Zapata, “Sistemas Multiagente”, Foundations, pp. 1-42, 2010.

C. Sanchez-Sanchez, H. Jimenez-Salazar, C. Rodriguez-Lucatero, E. Villatoro-Tello, and G. Ramrez-De-La-Rosa, “Sistema multiagente integrador de bibliotecas digitales’’, Res. Comput. Sci., vol. 93, pp. 45-56, 2015.

C. Padilla-navarro and M. R. Carlos, “Multi-agent system applied to prediction of personality on Twitter’’, vol. 115, pp. 147-156, 2016.

G. Wen, P. Wang, and S. Member, “Robust Neuro-Adaptive Containment of Multileader Multiagent Systems With Uncertain Dynamics’’, pp. 1-12, 2017.

C. Wei, J. Jing, C. Mei, and G. Shaoting, “Design of workshop production management control system based on Multi- Agent’’, IEEE, pp. 427-430, 2016.

J. Izquierdo, E. Campbell, and I. Montalvo, “Combinacion multi-agente de algoritmos evolutivos y minera de datos para mejorar la busqueda en problemas de optimizacion del mundo real’’, Congr. Metodos, 2015.

J. H. Taylor, “Intelligent Control and Asset Management: An Event-based Control Road Map’’, IEEE, 2016.

E. R. Nunez-valdez et al., “Plataforma de recomendacion de contenidos para libros electronicos inteligentes basada en el comportamiento de los usuarios’’, Vent. Informatica, vol. 14, pp. 25-40, 2012.

I. Brigui-Chtioui, P. Caillou, and E. Negre, “Intelligent Digital Learning: Agent-Based Recommender System’’, Proc. 9th Int. Conf. Mach. Learn. Comput., pp. 71-76, 2017.

E. Camilo, V. Aldana, J. Augusto, and C. Torres, “Tratamiento de datos para la aplicación de las TICS en escenarios de postconflicto: Elaboración de centros de memoria Histórica, cultural, museos y Senderos ecoturisticos de zonas afectadas por el conflicto con elementos de Realidad Mixta y Sistemas Recom”, 2018.

K. C. D. Monje and A. F. S. Cruz, “Análisis e implementación culturales y de virtualidad en contextos culturales y de contenido histórico”, 2017.

F. Ricci, L. Rokach, and B. Shapira, “Introduction to Recommender Systems Handbook”, in Recommender Systems Handbook, 2011, pp. 1-35.

S. Russel and P. Norvig, Inteligencia Artificial, Segunda Ed. Madrid: Prentice hall, 2004.

M. H. Moradi, S. Razini, S. M. Hosseinian, “State of art of multiagent systems in power engineering: A review”, Renewable and Sustainable Energy Reviews, vol. 58, 2016, pp. 814-824.

C. A. Ruiz, M. Susana, B. Damián, and J. Matich, “Redes Neuronales: Conceptos Básicos y Aplicaciones”, Universidad Tecnológica Nacional, Facultad Regional Rosario Departamento de Ingeniería Química Grupo de Investigación Aplicada a la Ingeniera Química (GIAIQ), 2001.

J. A. Botía, “Modelado de un Sistema Multi-Agente mediante la aplicación de la metodología INGENIAS con el Ingenias Development Kit”, 2007.

J. Hilera and V. José Martínez Hernando, Redes neuronales artificiales: fundamentos, modelos y aplicaciones / J.R. Hilera González, V.J. Martínez Hernando., Primera Edición. Madrid, 1995.

M. Kubat, “Neural networks: a comprehensive foundation by Simon Haykin, Macmillan”, The Knowledge Engineering Review, vol. 13, no. 4. p. S0269888998214044, 1999.

J. Arrieta, J. Torres, and H. Velásquez, “Predicciones de modelos econométricos y redes neuronales: el caso de la acción de SURAMINV”, Semest. Económico 12, pp. 95-109, 2009.

F. Pérez and H. Fernández, “Las redes neuronales y la evaluación del riesgo de crédito”, Revista Ingeniería Universidad de Medellín, pp. 77-91, 2007.

N. Torres, L. Hernandez, and L. Pedraza, “Redes neuronales y predicción de tráfico”, Rev. Tecnura, vol. 15, no. 29, pp. 90-97, 2011.

J. Heaton, “Introduction to Neural Networks with Java”, Heat. Res. Inc, 2008.

M. Gregori, J. Cámara, and G. Bada, “A jabber-based multi-agent system platform”, Proc. fifth Int. Jt. Conf. Auton. agents multiagent Syst., pp. 1282-1284, 2006.

A. Ballén, N. Gelvez, and H. Espitia, “Prototype of a recommendation system based on multi-agents in the analysis of movies dataset”, WEA 2018, p. 12, 2018.


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