Generation of human computational models with machine learning

Campuzano, Francisco; García-Valverde, Teresa; Botia Blaya, Juan A. y Serrano Fernández, Emilio (2015). Generation of human computational models with machine learning. "Information Sciences", v. 293 (n. 1); pp. 97-114. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2014.09.008.

Descripción

Título: Generation of human computational models with machine learning
Autor/es:
  • Campuzano, Francisco
  • García-Valverde, Teresa
  • Botia Blaya, Juan A.
  • Serrano Fernández, Emilio
Tipo de Documento: Artículo
Título de Revista/Publicación: Information Sciences
Fecha: Febrero 2015
Volumen: 293
Materias:
Palabras Clave Informales: Smart environments testing; Human behavior modeling; Machine learning; Social simulation
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,

Más información

ID de Registro: 35704
Identificador DC: http://oa.upm.es/35704/
Identificador OAI: oai:oa.upm.es:35704
Identificador DOI: 10.1016/j.ins.2014.09.008
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0020025514009049
Depositado por: Memoria Investigacion
Depositado el: 08 Jul 2015 07:10
Ultima Modificación: 17 Nov 2017 08:45
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