A Multi-agent architecture for labeling data and generating prediction models in the field of social services

Serrano Fernández, Emilio and Pozo Jiménez, Pedro del and Suárez-Figueroa, Mari Carmen and González Pachón, Jacinto and Bajo Pérez, Javier and Gómez Pérez, Asunción (2017). A Multi-agent architecture for labeling data and generating prediction models in the field of social services. In: "International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2017)", 21-23 Jun 2017, Oporto, Portugal. ISBN 978-3-319-60285-1. pp. 177-184. https://doi.org/10.1007/978-3-319-60285-1_15.

Description

Title: A Multi-agent architecture for labeling data and generating prediction models in the field of social services
Author/s:
  • Serrano Fernández, Emilio
  • Pozo Jiménez, Pedro del
  • Suárez-Figueroa, Mari Carmen
  • González Pachón, Jacinto
  • Bajo Pérez, Javier
  • Gómez Pérez, Asunción
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2017)
Event Dates: 21-23 Jun 2017
Event Location: Oporto, Portugal
Title of Book: Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems
Date: 2017
ISBN: 978-3-319-60285-1
Volume: 722
Subjects:
Freetext Keywords: Multi-agent systems; Human-agent societies; Social services; Machine learnin
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Prediction models are widely used in insurance companies and health services. Even when 120 million people are at risk of suffering poverty or social exclusion in the EU, this kind of models are surprisingly unusual in the field of social services. A fundamental reason for this gap is the difficulty in labeling and annotating social services data. Conditions such as social exclusion require a case-by-case debate. This paper presents a multi-agent architecture that combines semantic web technologies, exploratory data analysis techniques, and supervised machine learning methods. The architecture oers a holistic view of the main challenges involved in labeling data and generating prediction models for social services. Moreover, the proposal discusses to what extent these tasks may be automated by intelligent agents.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2016-78011-C4-4-RUnspecifiedUniversidad Politécnica de MadridDatos 4.0: retos y soluciones

More information

Item ID: 50227
DC Identifier: http://oa.upm.es/50227/
OAI Identifier: oai:oa.upm.es:50227
DOI: 10.1007/978-3-319-60285-1_15
Official URL: https://link.springer.com/chapter/10.1007/978-3-319-60285-1_15
Deposited by: Memoria Investigacion
Deposited on: 06 Feb 2019 10:32
Last Modified: 06 Feb 2019 10:32
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